Sensory consequences of our own actions are perceived differently from the sensory stimuli that are generated externally. The present event-related potential (ERP) study examined the neural responses to self-triggered stimulation relative to externally-triggered stimulation as a function of delays between the motor act and the stimulus onset. While sustaining a vowel phonation, subjects clicked a mouse and heard pitch-shift stimuli (PSS) in voice auditory feedback at delays of either 0 ms (predictable) or 500–1000 ms (unpredictable). The motor effect resulting from the mouse click was corrected in the data analyses. For the externally-triggered condition, PSS were delivered by a computer with a delay of 500–1000 ms after the vocal onset.
As compared to unpredictable externally-triggered PSS, P2 responses to predictable self-triggered PSS were significantly suppressed, whereas an enhancement effect for P2 responses was observed when the timing of self-triggered PSS was unpredictable.
These findings demonstrate the effect of the temporal predictability of stimulus delivery with respect to the motor act on the neural responses to self-triggered stimulation. Responses to self-triggered stimulation were suppressed or enhanced compared with the externally-triggered stimulation when the timing of stimulus delivery was predictable or unpredictable. Enhancement effect of unpredictable self-triggered stimulation in the present study supports the idea that sensory suppression of self-produced action may be primarily caused by an accurate prediction of stimulus timing, rather than a movement-related non-specific suppression.
A multitude of sensory stimuli are processed constantly by our sensory systems. It has been suggested that the sensory system allots more processing resources to unexpected stimuli, that often require our immediate reaction, than to stimuli that are the predicted consequences of our own actions . Previous research has demonstrated that the processing of sensory consequences of self-produced actions is different from that of externally-produced stimuli [2–4]. For example, responses to self-produced tactile stimuli are suppressed relative to externally-produced stimuli in the somatosensory cortex [2, 3]. The phenomenon that sensory responses to self-produced stimuli are weaker relative to externally-produced stimuli has been interpreted using an internal forward model [5, 6]. The central nervous system generates an efference copy  of the motor command as a prediction of sensory consequences of one's own action, and compares this prediction with the actual sensory feedback. When an accurate prediction of the actual sensory feedback is available, only a small prediction error between the intended motor action and the actual sensory feedback is generated, which in turn leads to a net cancellation of sensory input. When there is no efference copy or when the signals from the predicted and actual feedback do not match, a larger prediction error is generated, which translates to a larger response in the sensory or somatorsensory cortex.
Recently, several electrophysiological and neuromagnetic studies have demonstrated a similar suppression phenomenon in the auditory system. For example, self-triggered tones elicited suppressed event-related potentials (ERPs) or magnetoencephalography (MEG) responses as compared to the playback of the identical tones triggered by a computer [4, 8–11]. Several other MEG studies in humans have also demonstrated suppressed auditory cortical responses (e.g. M100) to self-produced speech as compared to the playback of pre-recorded speech [12–17]. Ford and her colleagues conducted a series of ERP studies to investigate the efference copy mechanisms of the auditory system in normal people and patients with schizophrenia [18–20]. The results showed suppressed N1 responses to self-produced speech compared with listening to the speech playback in the normal subjects, while suppression effect was not observed in patients with schizophrenia [18, 20]. In recent ERP studies where auditory feedback was pitch-shifted during vocalization, greater suppression effects (N1) of self-produced, unaltered voice were found compared with an altered voice or alien voice [21, 22]. These suppression findings suggest that the auditory cortex compares the actual auditory feedback against a prediction of expected feedback to distinguish self-produced speech from externally-produced sounds.
In a literature review of auditory suppression studies, the timing of self-triggered stimulation was usually predictable. For example, pure tones were presented immediately following the actions of a participant's button press. However, the onset of pure tones was unpredictable when they were triggered externally by the computer [9, 13, 17, 21]. This confound leaves open the possibility that suppressed processing of self-triggered actions may be due to a fact that humans can precisely detect the temporal patterns of auditory stimuli triggered by their own actions, while the sensory consequences of externally-generated actions cannot be predicted as such. If sensory suppression did not exist when the timing of self-triggered stimulation was as unpredictable as that of externally-triggered stimulation, the suppression effect may be primarily attributed to an accurate prediction of stimulus timing. Alternatively, self-triggered stimulation could result in a general suppression of sensory events that occur with the motor act. That is, sensory suppression is due to a non-specific suppression of sensory events that relates to the motor act, and suppression of self-triggered stimulation would be independent of the delays between the motor act and the stimulus onset.
To clarify the effect of temporal predictability on the neural processing of self-trigged stimulation relative to externally-triggered one, several studies have been conducted in the auditory modality. Schafer and Marcus reported a suppression effect for N1 responses to self-triggered click sounds even when the sound onset relative to the motor act was delayed up to 4 seconds by a fixed time . Bäß et al.  examined the cortical responses (N1) to self-triggered tones relative to the identical tones that were triggered externally, where the frequency and the onset of self-triggered tones were either predictable or unpredictable. Results showed that, even when the onset of self-triggered tones was unpredictable, N1 responses were still suppressed relative to externally-triggered tones, although the amount of suppression varied across conditions with the largest suppression for predictable frequency and predictable onset. These studies suggest that suppression of self-triggered stimulation may be due to a non-specific movement-related suppression of sensory signals.
Contrasting findings, however, were reported in several recent pitch-shifted ERP studies of self-produced vocalization [23–25]. Behroozmand et al. [23, 25] reported enhanced P2 responses during active vocalization relative to passive listening when pitch-shift stimuli (PSS) were triggered by the computer with a random delay after the vocal onset (unpredictable), and only when the PSS occurred immediately after the vocal onset (predictable) did suppression effect exist . Liu et al.  did a similar study but compared the neural responses to the self-triggered PSS with those triggered by the computer during vocalization and listening. A random delay between the mouse click and the PSS onset was introduced for the self-triggered task, and the PSS were also randomly triggered by the computer for the externally-triggered task. The results showed that unpredictable self-triggered PSS elicited larger N1/P2 responses than unpredictable externally-triggered PSS, indicating an enhancement rather than suppression effect of self-triggered stimulation . These studies suggest that enhanced brain activity can be elicited to distinguish unpredictable self-triggered from unexpected externally-triggered stimulation. It should be noted, however, that several factors could possibly confound the validity of the conclusions in these studies. For instance, Behroozmand et al. [23, 25] compared the neural responses during active vocalization with those during its playback; the cortical responses could be dampened due to the different physical qualities of the sounds resulting from the bone conduction during vocalization, middle ear muscle contraction, and the response characteristics of the ear . One of the primary limitations in Liu et al.  is that the motor responses resulting from the finger movement (i. e. mouse click) were not corrected due to the lack of a motor-only task as a control condition, although the authors argued that the motor responses would not affect the neural responses to the PSS that occurred 500–1000 ms after the mouse click. This assumption, however, was not validated in all previous research. Therefore, the enhancement effect of unpredictable self-triggered stimulation observed in Liu et al.'s study  could be due to the effect of the motor act on the neural responses rather than the random delays between the motor act and the stimulus onset.
Given these contrasting findings, the role of temporal predictability in distinguishing self-triggered from externally-triggered stimulation is controversial. Whether sensory suppression is due to an accurate prediction of self-triggered stimulation or a direct consequence of self-triggered stimulation itself remains unclear. Therefore, the present ERP study was designed to examine the effect of temporal predictability on the neural processing of self-triggered stimulation relative to unexpected externally-triggered stimulation during self-monitoring of vocal production. The altered auditory feedback protocol [26, 27] was used in this experiment: subjects vocalized a vowel sound and heard the PSS in voice auditory feedback triggered by a self- or externally-produced stimulation. Temporal predictability of self-triggered PSS was manipulated as predictable or unpredictable by introducing fixed or random delays between the motor act and the stimulus onset. For the externally-triggered stimulation, the PSS was triggered by a computer with a random delay after the vocal onset such that subjects were incapable of predicting when the PSS occurred. We expected that the temporal predictability of stimulus delivery relative to the motor act would modulate the neural processing of self-triggered stimulation relative to unpredictable externally-triggered stimulation.
Seventeen right-handed, native-Mandarin speakers (8 males and 9 females) participated in this study. They reported no history of speech, hearing, neurological disorders, or voice training. All subjects passed a hearing screening at the threshold of 25 dB HL for pure-tone frequencies of 0.5-4 kHz. Four subjects were excluded from the final analysis because of excessive artifacts such as alpha activity or ocular artifacts. So data from 13 subjects (7 males and 6 females, mean age 23.3 ± 1.8 years) were finally analyzed and reported. All the subjects read and signed the informed consent approved by the Institution Review Board of The First Affiliated Hospital at Sun Yat-sen University.
Subjects were seated in a sound-treated booth throughout the testing. Prior to the testing, acoustic calibration of the recording system was performed to insure that the intensity of voice feedback heard by the subjects was 10 dB (SPL) higher than that of subject's voice output. This 10 dB gain between voice and feedback signals was used to partially mask the air-born and bone-conducted voice feedback [22, 25]. Their voice signals were recorded through a dynamic microphone (Genuine Shupu, model SM-306). The microphone signal was amplified with a MOTU Ultralite Mk3 firewire audio interface and shifted by an Eventide Eclipse Harmonizer. MIDI software (Max/MSP, v.5.0 by Cycling 74), running on a Macintosh computer, was used to control the harmonizer by sending a command to produce no shift in pitch feedback or a decrease in pitch feedback to the subject. The delay time for the harmonizer to shift pitch was about 15 ms. The pitch is shifted in units of cents (cents = 100 × (39.86 × log10(F0/reference)); reference denotes an arbitrary reference note of 195.997 Hz (G4).) because this scale is logarithmically related to F0 and is constant relative to the absolute F0 of a given subject. The pitch-shifted voice signal was played back to subjects through insert plastic earphones (ER1, Etymotic Research Inc.). The voice, feedback and transistor-transistor logical (TTL) control pulses indicating the onset and offset of the stimulus were digitized at a sampling frequency of 10 k Hz by an A/D converter (PowerLab, model ML880, AD Instruments), and recorded using LabChart software (v.7.0 by AD Instruments).
During the experiment across all tasks, the subjects were instructed to vocalize a vowel sound (/a/, about 4 s duration) at their comfortable pitch and loudness. By instructing the subjects to vocalize without contracting speech muscles (e.g., tongue, lips or jaw), we greatly reduced the chance that such muscle contraction would affect the quality of the electroencephalogram (EEG) signals. During each vocalization, a PSS of −200 cents (100 cents = 1 semitone) with 200 ms duration was presented in voice auditory feedback. The subjects were asked to vocalize 100 times for each task and take a pause for about 3–4 seconds prior to the next vocalization to avoid vocal fatigue.
The experiment consisted of four tasks (see Figure 1). In the motor-vocal (MV) task, the subjects were instructed to click a mouse to trigger one PSS in the middle of the vocalization. During vocalization, they heard the PSS in voice auditory feedback at delays of 0 ms (predictable, MVP) or 500–1000 ms (random delay, MVR) after mouse click. Thus, the PSS onset was either predictable or unpredictable with respect to the motor act (mouse click). In the vocal-only (VO) task, the subjects vocalized the vowel while one PSS was triggered by the computer with a delay of 500–1000 ms (random delay, VOR) after the vocal onset. By doing this, the subjects were incapable of predicting the onset of the PSS. Subject's voice onset automatically activated the MIDI program using a locally fabricated Schimitt trigger circuit that detected a positive voltage on the leading edge of the amplified vocal signals. The output of this circuit was used to trigger the PSS at a delay of 500–1000 ms with respect to the vocal onset. The timing of the MIDI output from the onset of the pulse from the vocal detection circuit was about 25 ms. In the motor-only (MO) task, the subjects sustained the vocalization and clicked the mouse in a similar fashion as in the MVP and MVR tasks, but no PSS were presented and their voice pitch feedback was not altered. This task was used as a control condition to rule out the motor responses resulting from the finger movement. The sequence of the intervening four tasks (MVP, MVR, VOR, MO) was randomized across all the subjects.
Overview of the temporal characteristics of events in four experimental tasks. Pitch-shift stimuli were triggered at delays of 0 ms or 500–1000 ms after mouse click for motor-vocal task with predictable delay (A) and motor-vocal task with unpredictable delay (B), respectively. Vocal-only task with random delay is illustrated in (C), in which a stimulus was triggered by the computer at a delay of 500–1000 ms after the vocal onset. In the motor-only task as shown in (D), subjects clicked the mouse but no stimulus was delivered. The traces in (A)-(D) denote pitch-shifted voice F0 contours (cents) and TTL control pulses indicating the onset and offset of the stimulus.
The EEG signal was recorded with a 64-electrode Geodesic Sensor Net using a Net Amps 300 amplifier at a sampling rate of 1000 Hz (Electrical Geodesics Inc.) . The electro-oculogram (EOG) artifact was monitored with four electrodes placed above and below the eyes and at the outer canthus. Individual sensors were adjusted until impedances were less than 50 kΩ , and all electrodes were referenced to the vertex (Cz) during recording.
After data acquisition, the EEG was re-referenced to the average of electrodes on each mastoid and band-pass filtered at 1–20 Hz. The continuous EEG was segmented into an epoch starting at 200 ms before and terminating 700 ms after the PSS onset. Segmented files were scanned for artifacts (excessive muscular activity, eye blinks and eye movements) with the Artifact Detection toolbox in Net Station (v. 4.4) using a threshold of 50 μV. Individual electrodes were rejected if they contained artifacts of any kind in more than 20% of the segments. Artifact-free segments for correct responses were averaged and baseline corrected across all conditions. The amplitudes and latencies of N1 and P2 across all tasks were measured as the negative and positive peaks in the time windows of 80–150 ms and 150–280 ms after the PSS onset.
Specifically, by using the EEG signals for the MO task, the motor responses resulting from the finger movement with respect to the PSS onset were measured to correct for the motor effect in the motor-vocal task. For the MVP task, subjects clicked the mouse and heard the PSS at a zero delay, and the TTL pulses for the MO task indicated the onset of the mouse click/PSS. Thus, averaging the segmented trials relative to the onset of the TTL pulses for the MO task led to the motor response that occurred at the onset of the mouse click, which was used to correct for the motor activity in the neural responses to the PSS for the MVP task. For the MVR task, the PSS was delivered at a random delay after the mouse click. In order to measure the motor response relative to the PSS onset, we first extracted the relative timing values indicated by the difference between the onset of mouse click and the PSS onset. These timing values were sent to the EEG trials for the MO task to generate new TTL pulses that indicated the onset of the PSS that occurred at a random delay after the mouse click. The EEG signals for the MO task were re-segmented and averaged relative to the new TTL pulses, which in turn was used to correct for the motor activity in the neural responses to the PSS for the MVR task. These two motor responses for the MVP and MVR tasks are illustrated in Figures 2 and 3 as the dashed traces labeled MO_P and MO_R. As can be seen, there is a small positive shift in the MO_P, whereas the effect of motor act on the neural response is small for the MVR task. The motor responses were subtracted from the neural responses for the MVP and the MVR task respectively, and the corrected ERPs were used for further statistical analyses.
Grand averaged ERP waveforms over all subjects at electrodes C1, Cz, and C2 for motor-only with zero delay (MO_P, dashed line) task, original motor-vocal task with zero delay (MVP, thin solid line), and corrected motor-vocal task with zero delay (MVP_A, thick solid line). The response for the MVP_A condition was calculated by a subtraction of the response for the MO_P condition from that for the MVP condition.
Grand averaged ERP waveforms over all subjects at electrodes C1, Cz, and C2 for motor-only with random delay task (MO_R, dashed line), original motor-vocal task with random delay (MVR, thin solid line), and corrected motor-vocal task with random delay (MVR_A, thick solid line). The response for the MVR_A condition was calculated by a subtraction of the response for the MO_R condition from that for the MVR condition.
Repeated-measures analysis of variance (RM-ANOVA) was performed on the amplitudes and latencies of N1 and P2 components using SPSS (v.16.0). Since previous research showed prominent N1/P2 responses to the PSS centrally [23, 25], the amplitudes and latencies were analyzed from electrodes C1, Cz, and C2. Appropriate subsidiary RM-ANOVAs were calculated when higher-order interactions were observed. Probability values were corrected for multiple degrees of freedom using Greenhouse-Geisser if the assumption of sphericity was violated. Corrected p values were reported along with original degrees of freedom.
Figure 2 and 3 shows grand-averaged responses of ruling out the motor responses (MO task) from predictable and unpredictable self-triggered stimulations (MVP and MVR) at electrodes C1, Cz, and C2. The thick solid lines denote the adjusted responses (MVP_A and MVR_A), calculated by subtracting the motor responses (MO_P and MO_R) from the original responses (thin lines, MVP and MVR). In the subsequent text, the neural responses for the MVP and the MVR tasks were all corrected and tested for significance.
Figure 4 illustrates grand averaged ERP waveforms in response to the PSS across all tasks. The solid lines, the dotted lines, and the dashed lines indicate the ERP waveforms for the MVR, VOR, and MVP tasks. As shown in this figure, subjects produced similar N1 responses across all tasks, while the amplitudes of P2 responses varied as a function of task. The largest P2 amplitude was associated with the MVR task, followed by the VOR task and the MVP task.
Grand averaged ERP waveforms over all subjects at electrodes C1, Cz, C2. The solid lines, the dotted lines, and the dashed lines denote the ERPs for the MVR, VOR and MVP tasks. The ERPs for the MVP and MVR tasks have been corrected and showed as MVP_A and MVR_A.
A task × site RM-ANOVA on N1 amplitude showed a main effect of site (F(2, 24) = 3.475, p = 0.047). However, the main effect of task failed to reach a significant difference (F(2, 24) = 0.102, p = 0.819). A task × site interaction was not found either (F(4, 48) = 1.521, p = 0.211). Figure 5 (bottom) shows the distributions of N1 peak amplitudes across the task illustrating the topographic differences reported above.
Topographical distributions of the grand averaged ERPs to pitch feedback perturbations for the MVP (left), MVR (middle) and VOR (right) tasks. The distributions of P2 (latency: 227 ms) and N1 (latency: 124 ms) peak amplitudes relative to the onset of pitch-shift stimulus, are shown on the top and bottom, respectively.
T-bar plots showing the averaged values and standard errors of N1 and P2 amplitudes across three tasks. The black, the dotted, and the crossed T-bars denote the responses for the MVP, VOR, and MVR task, respectively. The asterisks indicate the significant difference between conditions.
A task × site RM-ANOVA on P2 amplitude showed significant main effects of task (F(2, 24) = 17.486, p < 0.001) and site (F(2, 24) = 23.295, p < 0.001) but not for task × site interaction (F(4, 48) = 1.797, p = 0.145). Multiple comparisons using Bonferroni adjustment showed that the P2 amplitude in the MVR task was significantly larger than that of the MVP (p = 0.001) and VOR task (p = 0.007) (see Figure 6). In addition, the MVP task elicited significantly smaller P2 amplitudes than the VOR task (p = 0.027) (see Figure 6). Figure 5 (top) shows the P2-distribution scalp maps for the MVP (left), VOR (middle) and MVR (right) tasks, illustrating the topographic differences across three tasks.
In addition, a task × site RM-ANOVA of N1 latency (124 ± 22 ms) revealed no main effect of task (F(2, 24) = 0.860, p = 0.436) or site (F(2, 24) = 0.250, p = 0.781). Similarly, statistical results showed no systematic changes of P2 latency (227 ± 28 ms) as a function of task (F(2, 24) = 0.747, p = 0.485) or site (F(2, 24) = 0.756, p = 0.480).
The present study examined the modulation of the neural responses to self-triggered stimulation as a function of temporal predictability relative to externally-triggered stimulation during vocalization. The results showed that, compared with externally-triggered stimulation (VOR), P2 responses to predictable self-triggered stimulation (MVP_A) were significantly suppressed, whereas an enhancement effect was found for P2 responses to unpredictable self-triggered stimulation (MVR_A). In addition, significantly suppressed P2 responses were elicited for the MVP task as compared to the MVR task. These findings support our hypothesis that sensory consequences of self-triggered auditory events can be modulated by the temporal predictability of stimulus delivery, resulting in a suppression or enhancement effect relative to externally-triggered stimulation.
Sensory suppression of self-triggered stimulation has been well documented in previous research on tactile stimulation [2, 3, 30], speech [12–17] and hearing [4, 10]. The present study is complementary to these findings by showing suppressed P2 responses to predictable self-triggered PSS compared with externally-triggered PSS during self-monitoring of vocal production. In addition, self-triggered PSS elicited suppressed P2 responses when the stimulus timing relative to the motor act was predictable than when it was not (i.e. MVP_A vs. MVR_A). Both of the results can be accounted for by the prediction-based forward model theory . That is, when timing of self-triggered stimulation was fully predictable (i.e. MVP task), a precise match between the intended auditory feedback (the efference copy) and the actual auditory feedback (the re-afference) generated a cancellation of auditory inputs, leading to suppressed neural responses relative to externally-triggered stimulation. When an accurate prediction of self-triggered pitch perturbation was not available in time (i.e. MVR task), the mismatch between the intended feedback and the actual outcome generated a large prediction error, resulting in a larger brain response compared with the MVP task. Suppressed responses to predictable self-triggered PSS in the present study may reflect the allocation of neural resources away from predictable stimuli when compared with unpredictable stimuli.
The major finding of the present study is that unpredictable self-triggered PSS elicited larger P2 responses than unpredictable externally-trigged PSS, leading to an enhancement effect of self-triggered stimulation. This finding reinforces one similar ERP study, showing larger P2/N1 responses to unpredictable self-triggered PSS than to unpredictable externally-triggered PSS during vocalization and listening . It is also complementary to other pitch-shift studies showing the vocalization-induced enhancement effect [23, 25]. Previous research in the somatosensory cortex indicated decreased sensory suppression to self-triggered tactile stimulation when the accuracy of sensory prediction in time and/or space was reduced . In an analogous study, Aliu et al.  reported suppressed M100 responses to self-triggered tones for zero delays between the tone onset and the button press, but this suppression did not generalize to nonzero delays conditions. Results from the present study further demonstrate that, when sensory prediction to self-triggered stimulation was unavailable, neural responses were no longer suppressed but enhanced relative to unpredictable externally-triggered stimulation. This finding provides supportive evidence to the idea that sensory suppression of self-triggered stimulation may be primarily attributed to an accurate prediction of stimulus timing, rather than a movement-related nonspecific suppression of response to a sensory stimulus.
One question arising from this finding has to do with the mechanism of the enhancement effect. According to the forward model, a larger prediction error between the efferene copy and the re-afference results in a larger brain response in the sensory or somatosensory cortex . For the MVR and VOR tasks in the present study, however, the forward model is unable to determine and compare the size of the prediction errors arising from these two tasks since in neither case was the stimulus delivery predictable. Thus, the enhancement effect of unpredictable self-triggered stimulation is not in line with the forward model. One plausible explanation is the selective attention related to attended and unattended times. It is suggested that that responses in the auditory cortex cannot be solely modulated by the features of acoustic stimulus but also affected by cognitive functions such as attention, memory and imagery . It has been demonstrated that anticipation of an event can influence the perception of the event  and attention can modulate the processing of sensory inputs in both space and time [35–37]. For example, larger N1 responses were found when stimuli were presented at attended times compared with unattended times . Similarly, tones presented at the attended ear elicited larger M1 responses than did unattended ear . In the present study, as compared to unpredictable stimulation triggered externally, subjects may have anticipated presentation of unpredictable PSS triggered by self-initiated actions. Therefore, unpredictable self-triggered stimulation may have caused the allocation of more attentional resources, and the P2 enhancement effect may be attributed to a favored processing of auditory pitch feedback arising from more attention to self-triggered stimulation. If this speculation is true, distinguishing sensory consequences of self-triggered actions from those triggered externally may not be fully accounted for by the predicted-based internal forward model. Rather, higher cognitive function such as attention may also play an important role in this process. Supportive evidence comes from recent studies in the visual system showing that the neural responses to both predicted and unpredicted stimuli in the case of attention were larger than to the same stimuli in the absence of attention, and predicted stimuli even elicited larger responses than unpredicted stimuli in the case of attention . This interaction between attention and prediction can be translated to the auditory system needs to be further explored.
The enhancement effect of self-triggered stimulation in the present study is in contrast with several studies where the suppression effect was still observed when temporal predictability of self-triggered stimulation was varied [4, 10]. The inconsistency between the present study and Aliu et al.  could be attributed to the way the temporal predictability of stimulus delivery was manipulated. In Aliu et al. , the tone was presented at short fixed delays (e.g. 100 ms) after button press, and subjects may not have been aware of such short fixed delays and the stimuli in these conditions were as if they occurred with a zero-delay condition (i.e. predictable). Moreover, suppression effect for nonzero delays condition was not observed when the number of trials was 100 but reached significance in the case of 300 trials, indicating a learning effect on the development of suppression of self-triggered stimulation . Bäß et al.  reported suppressed N1 responses to self-triggered stimulation regardless of the temporal predictability of self-triggered tones. Note that their results showed significantly reduced N1 responses to unpredictable self-triggered sound onset as compared to the predictable sound onset, which was responsible for a suppression effect of the unpredictable onset of sound presentation relative to externally-triggered sound. This finding is opposite to our results and other previous studies [25, 39], where neural responses to self-triggered stimulation were larger when stimulus timing was unpredictable than when it was predictable.
Another factor such as experimental task could also account for the contrasting findings between the present study and Bäß et al. . The present study examined the effect of temporal predictability on the neural processing of self-triggered stimulation during self-vocalization, whereas Bäß et al.  addressed this effect in a listening-to-tone task. It is thus speculated that the effect of temporal predictability on the neural processing of self-triggered stimulation may be task-dependent. This speculation is supported by two recent observations. In a study examining the neural responses to PSS during active vocalization and passive listening, Behroozmand et al.  found that active vocalization showed enhanced P2 responses only to non-zero delayed PSS compared with passive listening, which is complementary to the enhancement effect of unpredictable self-triggered stimulation in the present study. By contrast, Lange  compared the N1 response to self-triggered tones with those to equally predictable visually cued tones and found suppressed N1 responses to self-triggered tones irrespective of whether the onset of sound presentation was predictable or not, which parallels the results reported by Bäß et al. . Therefore, there may be a task-dependent manner of temporal predictability on the neural processing of self-triggered stimulation. Whether the present findings can be translated to the listening-related tasks remains to be investigated.
In addition, the PSS across three vocal tasks were presented to voice pitch feedback at different times with respect to the onset of vocalization, which could confound the results. In previous research of the efference copy mechanisms during speech [13, 21–23], the acoustic stimuli occurred at the same time relative to the vocal onset during active vocalization and passive listening. In the present study, however, the variability in the PSS presentation relative to the vocal onset was unavoidable because of the difference between self-paced mouse click and the computer triggering. In order to rule out this confounding factor, future experiments should be conducted to testify the effect of the delay between the vocal onset and stimulus presentation on the neural processing of voice pitch feedback during vocalization.
In the present study, the suppression/enhancement effect of self-triggered stimulation was reflected in the P2 response. Similarly, other pitch-shift ERP studies also showed the modulation of neural responses to pitch perturbation at P2 across tasks [23, 25, 41]. By contrast, previous auditory research shows prominent suppression effect at N1 [8, 10, 11, 39, 40]. It is speculated that the observation of a P2 effect in the present study and a N1 effect in other studies can be due to the specific demands of different tasks. In the present study, subjects sustained a vowel phonation and heard their pitch feedback shifted at the mid-utterance. It has been demonstrated that subjects compensate for pitch errors by changing their voice pitch in the opposite direction to the stimuli [26, 42]. It has been suggested that P2 has multiple generators with a center of activity near Heschl's gyrus , which overlaps the neural network for the correction of pitch errors during vocal motor control [44, 45]. Thus, given the involvement of those sources of P2 generation in the vocal motor control, it may account for the P2 effect observed in the present study as well as other pitch-shift studies during vocalization [23, 25, 41].
It is also noted that some pitch-shift studies reported N1 suppression of active vocalization relative to passive listening [21, 22], but this suppression only occurred when the PSS was presented at the vocal onset. If it was presented in mid-utterance, an enhancement effect was found for P2 only . This could be due to the differential mechanisms of vocal motor control at utterance onset and mid-utterance . At utterance onset, auditory feedback is compared with the efference copy predicted by a forward model to reach a pre-planned pitch goal, and a prediction error results in a suppression effect that is similar to other listen-to-tone studies. It has been suggested that N1 has multiple generators in primary and secondary auditory cortex [47, 48] and is considered to reflect the automatic detection of acoustic changes , which may account for the prominent suppression effect at N1. Therefore, the lack of an N1 effect in the present study does not challenge previous findings regarding N1 suppression of self-triggered tones, but our speculation of task dependency in the suppression/enhancement of the N1/P2 during self-triggered stimulation needs to be further explored.
The present study examined the differential processing of self- and externally-triggered stimulation as the delays between the motor act and the PSS onset were manipulated during self-monitoring of vocal production. As compared to unpredictable externally-triggered PSS, P2 responses to predictable self-triggered PSS were suppressed, whereas an enhancement effect for P2 responses was found in the case of unpredictable self-triggered PSS. These results demonstrate that the neural processing of self-triggered stimulation can be modulated as a function of temporal predictability of stimulus onset related to the motor act. The finding of an enhancement effect for unpredictable self-triggered stimulation provides supportive evidence to the idea that suppression of self-triggered stimulation could be primarily due to an accurate prediction of stimulus timing rather than a movement-related nonspecific suppression.
Zhaocong Chen, Xi Chen contributed equally to this work.
This work was supported by National Natural Science Foundation of China (No. 30970965 and 31070990) and Natural Science Foundation of Guangdong Province (No. 9151008901000053).
ZC and HL conceived and designed the experiments. ZC, XC, and PL performed the experiment and analyzed the data. ZC and DH contributed the materials and analysis tools. ZC, DH, and HL wrote the paper. All the authors read and approved the final draft.


The group, with 55 members, includes about a quarter of House Democrats. Rep. Rick Larsen, another Puget Sound Democrat representing a district with Navy facilities, also is a member.
Kilmer holds a district once represented by retired Rep. Norm Dicks, who nurtured military facilities on the peninsula and at Joint Base Lewis-McChord in his 36-year political career.


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A: Здравствуйте!
За "красивые кнопки и оформление текста" (впрочем, за оформление любого элемента вашего сайта) отвечает файл CSS.
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redpajama_set_name: RedPajamaStackExchange


text:
/**
 * @author Maximilian Greschke <maximilian.greschke@gmail.com>
 *
 */

'use strict';

import chai from 'chai';
import nock from 'nock';

const FastBill = require('../index');
const expect = chai.expect;
const assert = chai.assert;

let fastbill = FastBill.instantiate({
    email: "test@test.com",
    apikey: "abc123"
});

describe('The FastbillAPIs Subscription Interface', function () {

    afterEach( cb => {
        nock.cleanAll();
        cb();
    });

    describe('Subscription.get', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('get')).to.equal(true);
        });

        it('should respond with a list of subscriptions', function (done) {

            // set up mock response
            let scope = nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        SUBSCRIPTIONS: ['object', 'object', 'object'],
                        ERRORS: null
                    }
                });

            let options = {some: 'object'};
            let promise = fastbill.subscription.get(options);
            promise.then(function (subscriptions) {
                assert.isArray(subscriptions, 'Returns a list of subscription objects.');
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });

        });
    });

    describe('Subscription.create', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('create')).to.equal(true);
        });

        it('should respond with a fastbill subscription id', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        SUBSCRIPTION_ID: 1,
                        ERRORS: null
                    },
                    SUBSCRIPTION_ID: 1
                });

            let newSubscription = {
                ITEMS: ['test'],
                CUSTOMER_ID: 1
            };
            let promise = fastbill.subscription.create(newSubscription);
            promise.then(function (subscriptionId) {
                assert.typeOf(subscriptionId, 'number', 'Returns a subscription_id.');
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });

        });

    });

    describe('Subscription.update', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('update')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        STATUS: 'success',
                        ERRORS: null
                    }
                });

            let id = 1,
                modification = {CURRENCY: 'EUR'};
            let promise = fastbill.subscription.update(id, modification);
            promise.then(function (result) {
                expect(result).to.equal(true);
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });
        });

    });

    describe('Subscription.remove', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('remove')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        STATUS: 'success',
                        ERRORS: null
                    }
                });

            let id = 1;
            let promise = fastbill.subscription.remove(id);
            promise.then(function (result) {
                expect(result).to.equal(true);
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });

        });
    });

    describe('Subscription.setUsageData', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('setUsageData')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        ERRORS: null
                    }
                });

            let id = 1,
                usage = {some: 'usage'};
            let promise = fastbill.subscription.setUsageData(id, usage);
            promise.then(function (result) {
                expect(result).to.equal(true);
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });
        });
    });

    describe('Subscription.changeArticle', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('changeArticle')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        ERRORS: null
                    }

                });

            let id = 1,
                modification = {some: 'modification'};
            let promise = fastbill.subscription.changeArticle(id, modification);
            promise.then(function (result) {
                expect(result).to.equal(true);
                done();
            }, function (err) {
                done(
                    new Error('Promise should be resolved')
                );
            });
        });
    });

    describe('Subscription.getUsageData', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('getUsageData')).to.equal(true);
        });

        it('should respond with the a usage data object', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        USAGEDATA_ID: 1,
                        CUSTOMER_ID: 1,
                        SUBSCRIPTION_ID: 1
                    }

                });

            let id = 1;

            fastbill.subscription.getUsageData(id)
                .then(function (result) {
                    console.log(result);
                    assert.typeOf(result, 'object', 'Responds with an object.')
                    done();
                }).catch(err => {
                    done(err);
                });
        });
    });

    describe('Subscription.delete_usage_data', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('deleteUsageData')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        SUBSCRIPTION_NUMBER: 1,
                        ERRORS: null
                    }
                });

            let id = 1;
            fastbill.subscription.deleteUsageData(id)
                .then(result => {
                    expect(result).to.equal(true);
                    done();
                })
                .catch(err => {
                    done(err);
                });

        });

    });

    describe('Subscription.reactivate', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('reactivate')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        SUBSCRIPTION_NUMBER: 1,
                        ERRORS: null
                    }
                });

            let id = 1;
            fastbill.subscription.reactivate(id)
                .then(function (result) {
                    expect(result).to.equal(true);
                    done();
                }).catch(err => {
                    done(err);
                });
        });
    });

    describe('Subscription.cancel', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('cancel')).to.equal(true);
        });

        it('responds with a cancellation date', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        CANCELLATION_DATE: Date.now(),
                        ERRORS: null
                    }
                });

            let id = 1;
            fastbill.subscription.cancel(id)
                .then(function (cancellationDate) {
                    assert.typeOf(cancellationDate, 'number', 'Responds with a cancellation date.');
                    done();
                }).catch(err => {
                    done(err);
                });
        });
    });

    describe('Subscription.getUpcomingAmount', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('getUpcomingAmount')).to.equal(true);
        });

        it('should respond with a total amount due', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        TOTAL: 200.0
                    }
                });

            let filters = {some: 'filters'};
            fastbill.subscription.getUpcomingAmount(filters)
                .then(function (total) {
                    assert.typeOf(total, 'number', 'Responds with total amount due.')
                    done();
                }).catch(err => {
                    done(err);
                });
        });
    });

    describe('Subscription.renew', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('renew')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri).post('').reply(200, {
                RESPONSE: {
                    ERRORS: null
                }
            });

            let id = 1;
            fastbill.subscription.renew(id).then(function (result) {
                expect(result).to.equal(true);
                done();
            }).catch(err => {
                done(err);
            });
        });
    });

    describe('Subscription.setaddon', function () {
        it('should be defined', function () {
            expect(fastbill.subscription.constructor.prototype.hasOwnProperty('setaddon')).to.equal(true);
        });

        it('should not respond with an error', function (done) {

            // set up mock response
            nock(fastbill.subscription.$uri)
                .post('')
                .reply(200, {
                    RESPONSE: {
                        TOTAL: 200.0
                    }
                });

            let id = 1,
                addon = {some: 'addon'};

            fastbill.subscription.setaddon(id, addon)
                .then(function (result) {
                    expect(result).to.equal(true);
                    done();
                }).catch(err => {
                    done(err);
                });
        });
    });
});

redpajama_set_name: RedPajamaGithub


text:
"The artist Christopher Wool is never through with a form just because he's used it before. Rather, in a perpetual cycle of self-appropriation, he runs the visual elements he creates through numerous incarnations, constantly experimenting with shifts in scale and medium… His process lends a ridged texture to the works, one that resembles the output of a printer running low on ink, though the effect is achieved here through a more old fashioned, and unique, kind of printing.
Indeed, much of the meaning of Wool's work in general, can be found in the tension between the unique, spontaneous gesture and the deliberate, mediated multiple. Wool's original boozy splash evokes action painting, but the result is reiterated across so many different mediums and scales that it becomes more mechanized than expressive.
Wool is not just playing formal games…rather, his work is an investigation into the relevance of singularity in an era of mechanical reproduction. [It] suggests that there is no definitive resolution, an appropriate attitude for an artist who once described himself as "more interested in questions than answers." – Samantha Friedman, Assistant Curator, MoMA, Department of Drawings and Prints
Prominent skateboard manufacturer Supreme continues their long history of collaborating with famous artists in this set of three skate decks. Christopher Wool's skate decks feature the artist's largely neutral color palette and abstract yet suggestive shapes for which he is known.
Purchase Artwork | Contact Ushe is known.

redpajama_set_name: RedPajamaCommonCrawl


text:
Rothstein Kass to Sponsor Scholarship Fund
Our Education Foundation
Rothstein Kass, a leading professional services provider, and Garden State Woman founder Judy Chapman have announced that the organizations will partner to sponsor the 2011 / 2012 Garden State Woman Education Foundation Scholarship Fund. The scholarship program supports inner-city New Jersey girls that have been accepted to a four-year New Jersey college or university. Presently the program has seven scholars; six at Rutgers and one at William Paterson University.
"On behalf of our young women scholars, we are extremely grateful for this philanthropic support. These students are dedicated, hard-working and motivated. They are all vibrant young people who will make a difference," said Ms. Chapman. "It is inspiring to realize that a firm such as Rothstein Kass appreciates the importance of giving back to our young people."
New and past recipients will be honored at the 7th Annual Garden State Woman Annual Awards Luncheon will be held on Friday, March 23, 2012 at the Park Avenue Club in Florham Park, New Jersey. The benefit is sponsored by the Garden State Woman Education Foundation to honor outstanding New Jersey women nominated by their peers in recognition of their professional and personal activities that benefit other women.
Rosalie Mandel, Founder of the Rothstein Kass The Office of LIFE Program (Leadership, Inspiration, Family, Empowerment) has previously been honored as Garden State Woman of the Year in recognition of the programs efforts to help women reach their full potential by fostering leadership opportunities, creating new business development platforms and facilitating mentoring relationships.
"When we launched the LIFE Program, a firm-wide initiative focused on supporting gender-related training, career development, communication and work/life integration issues, we hoped that it would provide Rothstein Kass with more opportunities to make a difference in the lives of motivated and talented women. We are proud of the impact that the program has made within our organization, and of our broader efforts to promote teamwork and facilitate work / life integration," said Steve Kass, Co-CEO of Rothstein Kass. "Partnering with Garden State Woman on its Education Foundation Scholarship Fund gives us an opportunity to share our success by supporting promising young women who will be the business leaders of the future."
About Rothstein Kass
Rothstein Kass is a premier professional services firm that has served privately held and publicly traded companies, as well as high-net-worth individuals and families, for more than 50 years. As trusted advisors to our clients, Rothstein Kass provides accounting, auditing and tax services, as well as a full array of integrated services, to clients across industry spectrums and in all stages of organizational development. At the core of Rothstein Kass' remarkable success lies our commitment to hiring, developing and retaining employees that represent an entrepreneurial spirit mirroring that of the sophisticated business and financial services communities that we serve.
About Garden State Woman
Garden State Woman, Inc. launched in 1998 as a publisher of a quarterly magazine aimed primarily at New Jersey corporate, professional and business owning women. Over the years, under Judy Chapman's leadership, Garden State Woman expanded services provided to New Jersey women into: hosting financial and health conferences and networking events; publishing annual financial and health guides; developing an information rich web site and creating the Garden State Woman Education Foundation 501(c)( 3).
Rosalie Mandel, CPA
You CAN Get There From Here


text:
Volume 3, Issue 1 (Autumn 2017 -- 2017) HDQ 2017, 3(1): 51-56 | Back to browse issues page
Dargahi A, Bagheri S, Poursadeghiyan M, Hosseini Ahagh M M, Farrokhi M. Knowledge and Attitudes of Students in Khalkhal Medical Sciences Faculty on Health Actions in Emergencies. HDQ. 2017; 3 (1) :51-56
Abdollah Dargahi1, Susan Bagheri2, Mohsen Poursadeghiyan3, Mir Mohammad Hosseini Ahagh4, Mehrdad Farrokhi *5
1- Department of Environmental Health Engineering, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran.
2- Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
3- Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
4- Department of Public Health, Khalkhal School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran.
5- Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. , me.farrokhi@uswr.ac.ir
Keywords: Knowledge and attitude, Health measures, Emergencies, Students
Full-Text [PDF 555 kb] (1170 Downloads) | Abstract (HTML) (4725 Views)
Full-Text: (972 Views)
Disaster is the condition in which the facilities required to have a normal life are lost as a result of a sudden natural and abnormal process and the ability of society in supplying health care is highly reduced [1]. The incidence of natural disasters such as floods, earthquakes, storms, etc., often has devastating effects on human societies and causing heavy casualties on their inhabitants [2].
According to the World Health Organization (WHO) in 2008, in the past 100 years, about 81 significant incidents occurred in Iran which killed approximately 160,000 people, wounded more than 170 thousand people and injured more than 44 million people [3]. Iran is prone to the incident and is one of the ten most incident-ridden countries in the world regarding the natural disasters. According to statistics, among the 43 infamous events, 34 of them have occurred in Iran [4, 5]. Iran is one of the world's most incident-ridden countries because it is located on the earthquake belt and frequently terrible earthquakes occur, the worst of them were earthquakes in Bam and Roodbar. Secondly, due to the existence of numerous rivers, floods arise from time to time.
In addition to many economic and psychological injuries, these natural disasters cause the displacement of many people due to the destruction of buildings and infrastructure of life like food stores and healthcare organizations. However, these unwanted phenomena cause mortal and financial losses, but a quick review and environmental health interventions can minimize its amount [4, 5]. After most of the disasters, many people are exposed to adverse environmental effects. One of the primary needs is the immediate creation of the best facilities for improving environmental health control to the extent which conditions and resources make it possible [6].
Emergency measures recommended are the same health measures in a normal condition which are simplified. Emergency health measures, which should be taken into consideration including monitoring the shelter for the injured, enough access to safe and healthy drinking water, food hygiene, wastewater feces and garbage disposal, fighting against pathogenic carriers, evaluation of the risk of post-disaster epidemics, etc. [7-10]. It is clear that one of the most fundamental needs, which should be considered in the relief operation is providing the most up-to-date recovery facilities and environmental health control, to the extent that existing resources and facilities make it possible [9]. The success rate of emergency control requires the need for community knowledge and the cooperation of various specialties. Essential interventions such as health education, environmental awareness, social requirements, and budget increase in development are necessary to prevent the prevalence of common diseases in camps [11, 12].
The success rate of emergency management requires community knowledge, the cooperation of various specialties, and training in the field of environmental health activities such as water quality control, food control, waste collection methods, collecting and sanitizing sewage, monitoring and assistance in the construction of sanitary toilets, health baths, monitoring and assistance in the burial of corpses and carcasses, and personal protection in crisis situations [13].
Knowledge and attitude of individual's health measures in emergencies reduce the effects of the crisis, and the purpose of measuring people's knowledge and attitude is to achieve knowledge of individuals, and if necessary, educating health care programs to improve the situation in emergencies. Few studies were conducted regarding the knowledge and attitude of individuals in the field of health measures in emergencies among students in Iran. This study aims to investigate knowledge and attitudes of Khalkhal Medical School students about health measures in emergencies.
This research is a descriptive study which was conducted in cross-section form in the 2013-2014 academic year. The population studied included four undergraduate students in semester 5 and higher (environmental health, public health, nursing, and midwifery) in Khalkhal Medical School. Based on the information of the deputy of the faculty of education, the number of students in semester 5 and higher was determined to be 200. Sampling was conducted by census method, and data collection was carried out using a questionnaire. In this questionnaire, instrument validity was tested using Content Validity.
To determine the reliability of this research, the test-retest method was used. In this test, the questionnaire was first given to 10 students, and then the test was repeated for the same students in 10 days and Pearson correlation coefficient was used to evaluate reliability of the questionnaire. Cronbach's alpha coefficient was 0.8 for the Knowledge Questionnaire and 0.7 for the Attitude Questionnaire. The questionnaire was arranged in three sections: 1. Demographic information, 2. Contained 10
knowledge questions, and 3. Included 10 attitude questions. About questions, the level of knowledge to each correct answer obtained score 1 and the incorrect answer and "I do not know" obtained zero score.
For questions of attitude, scoring for each phrase was done using a Likert scale and the grading criterion of 5, 4, 3, 2, and 1. This scale had 5 items as "I totally agree", "I agree", "No idea", "I disagree", and "I totally disagree." The maximum score of attitude questions was 50. Moreover, to classify aggregate knowledge scores, four grades (Less than 2: Poor; 3-5: Moderate; 6-8: Good; and more than 8: Excellent) and for attitude scores, four grades (Less than 25: Poor; 26-30: Moderate; 31-35: Good; and more than 35: Excellent). Trained experts distributed the questionnaire among the students. One-way ANOVA, Chi-square, and Independent T-test were used to compare mean scores in different groups.
In this study, 200 students of Khalkhal Medical School were examined for age and field of study (Table 1). Students' response to questions about health measures in emergencies is shown in Table 1. According to the results, among the questions of knowledge, question 7 (The simplest way to disinfect water by the people themselves in an emergency) had the highest number of respondents with 82%, and question 9 (The best point of influence of health observer in emergencies with the lowest human resources in food hygiene) had the lowest number of respondents with 8.5% (Table 2). More than 70% of students had sufficient knowledge about health measures in an emergency.
The highest degree of student knowledge obtained in nursing, public health, midwifery, and environmental health disciplines, respectively. The highest score of knowledge was obtained in the age group of 21-24 years old. Students' response to questions about attitudes toward health measures in emergencies is shown in Table 3. According to the results, among the questions of attitude, question 2 (Air conditioning inside the tent, the best natural ventilation method) had the highest frequency of respondents regarding "I totally agree" (Table 3).
There was a significant difference in student's attitude among different disciplines (P<0.05) in a way that the highest score of attitude was obtained in public health, midwifery, environmental health and nursing disciplines, respectively. Total scores of attitude regarding age group had a statistically significant difference (P<0.05). The highest Attitude score was obtained in the age group of 21-24 years old (Table 4). Moreover, the ratings of knowledge and attitudes about health measures in emergencies are presented in Figure 1. As the results showed, the highest level of grading was in the relevant knowledge section with a moderate level (65.5%) and in the attitude section, it was related to the "excellent" level of scoring at a rate of 47%.
The purpose of this study was to investigate the knowledge and attitude of Khalkhal Medical School students towards the field of health measures in emergencies. The results showed that in some of the above factors, students had high knowledge and in some others had a low level of knowledge of health measures in emergencies. For example, more than 80% of students had knowledge about the simplest ways to disinfect water themselves in an emergency. Low level of knowledge about the most appropriate way to fight mosquitoes in emergencies for a long time in the camps was in 67% students. On the other hand, 8% of students also had a low attitude concerning the difficulty of laboratory control on food and water in an emergency.
A study was conducted by Vosughi et al., showed that 11.2% of students had a high level of knowledge, 66.7% had average knowledge, and 22.1% had poor knowledge [14]. These results are consistent with the results of the present study. In this study also, 8% of students had a good level of knowledge, 65.5% had moderate level knowledge, and 26% had a poor level of knowledge. In addition, the results of study by Vosoughi et al., showed that 43% of staffs had good level of knowledge, 46% had moderate and 11% had poor level of knowledge towards health measures in emergencies [15].
In a study by Biglari et al., the results showed that 39% of participants had a good level of knowledge, 35.5% had an average, and 25.5% had a low level of knowledge [16]. Also a study by Rezaei et al, concluded that 47.3% of the subjects studied had very little information about the natural disasters and only 4.3% of the people had useful information in this regard [17].
The results of investigating students' Knowledge towards health measures in emergencies from different disciplines' point of view showed that the highest and lowest level of knowledge was in the field of nursing and environmental health, respectively. In a study by Khosh Nazar [18], 93.9% of nursing graduate students and the study by Imani [19] in Bandar Abbas, more than 80% of nurses had a low or moderate level of knowledge on crisis management and preparedness for accidents and disasters. These results are not consistent with the results of the current study.
The results of the study showed that there is no significant difference in levels of knowledge and attitude towards health measures in emergencies among students. Also, the results showed that some fields of study had a relatively low level of knowledge about health measures in emergencies. Therefore, given the importance of this issue, it is essential that extraordinary measures be taken to raise awareness and attitudes of students concerning emergency measures.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Hereby, authors express their gratitude and appreciation towards all the students of the Khalkhal Medical School participated and cooperated in the implementation of this research.
The authors declared no conflicts of interest.
[1]Chapman K, Arbon P. Are nurses ready. Australasian Emergency Nursing Journal. 2008; 11(3):135–44. doi: 10.1016/j.aenj.2008.04.002
[2]Daneshmandi M, Amiri HO, Vahedi MA, Farshi MA, Saghafi A, Zigheymat F. Assessing the level of preparedness for confronting crisis such as flood, earthquake, fire and storm in some selected hospitals of Iran. Military Medicine Journal. 2010; 12(3):167-71.
[3]Haji zadeh M, Seyyed Mazhari M, Pishgooie A, Aliyari S. [The effect of buddy aid education packages on the wives of military personnel awareness and attitude in the face with earth quake in 2013 (Persian)]. Military Caring Sciences. 2014; 1(1):1–8. doi: 10.18869/acadpub.mcs.1.1.1
[4]Seyedin H, Abbasi Dolatabadi Z, Rajabifard F. Emergency nurses' requirements for disaster preparedness. Trauma Monthly. 2015; 20(4). doi: 10.5812/traumamon.29033
[5]Nasiripour AA, Raeissi P, Mahboubi M. Assess preparedness of border hospitals to deal with the borders crisis in Kermanshah province. Quarterly Journal of Health Administration. 2007;10(28):41-8.
[6]Asl Hashemi A. Healh Actions in Emergencies (1). Tabriz: Tabriz University of Medical Sciences; 1995.
[7]Asl Hashemi A. [The course of providing emergency health measures in increasing students' knowledge and attitudes (Persian)]. Journal of Medical Education Development. 2009; 1(1):9-15.
[8]Dargahi A, Atafar Z, Karami A, Hoseini Ahagh MM, Zaebi Y, Amirian F, et al. knowledge and attitudes of Kermanshah University of medical sciences students in the field of food hygiene and safety. International Journal of Pharmacy & Technology. 2016; 8(2):13877-13890
[9]Farrokhi M, Dolatabadi ZA, Pakjouei S, Pouyesh V. Approaches to post-disaster environmental recovery. Health in Emergencies & Disasters Quarterly. 2016; 1(2):65-70.
[10]World Health Organization. Environmental health in emergencies. Geneva: World Health Organization; 2007.
[11]Becker SM. Environmental disaster education at the university level: An integrative approach. Safety Science. 2000; 35(1-3):95–104. doi: 10.1016/s0925-7535(00)00025-4
[12]Abu Mourad T. Palestinian refugee conditions associated with intestinal parasites and diarrhoea: Nuseirat refugee camp as a case study. Public Health. 2004; 118(2):131–42. doi: 10.1016/j.puhe.2003.09.002
[13]Jourvand R, Sadeghirad K, Golami OA, Vejdani M, Panahi R, Heydarabadi AB. Disasters preparedness of health workers in Dehloran, Iran. Journal of Health in the Field. 2017; 3(3):13-18.
[14]Vosoughi Nayyeri M, Asgari M, Jahed Khaniki GH, Dargahi Gh, Golestani far H, Parastar S. [Investigation of knowledge and attitudes of students in Tehran University of Medical Sciences on health actions in emergencies (Persian)]. Quarterly Scientific Research Journal of Rescue & Relief. 2012; 4(2):43-51.
[15]Vosoughi Niri A, Vosoughi Niri M, Golestanifar H, Pahlovanzadeh B, Savadpoor MT. Investigation of army soldiers readiness about health actions in critical circumstances, Case study: Malek Ashtar Military Garrison's. Health in Emergencies and Disasters. 2013; 1(1).
[16]Biglari H, Hami M, Yari A, Poursadeghiyan M, Farrokhi M. Awareness of medical students of Gonabad University about nutrition and food preservation in disasters. Health in Emergencies and Disasters Quarterly. 2017; 2(3): 133-8. doi: 10.18869/nrip.hdq.2.3.133
[17]Rezaee Ashtiyani AA, Hadi MA. [Details of Arak citizens in dealing with in the event of natural disasters (Persian). Paper presented at the 6th National Congress on Environmental Health. 22-24 October 2003; Sari, Iran.
[18]Khoshnazar T, Moghadamnia M, Khankah H, Ahmadi S. Awarencess of nursing students toward disaster preparedness in the health care system. Paper presented at the 5th International Congress on Health and Crisis Management in Disasters, 24-26 January 2011, Tehran, Iran.
[19]Imani E, Hosseini Teshnizi S, Tafrihi M, Alavi A, Jafari A, Badri Sh, et al. [Nurses' knowledge about crisis management and its related factors (Persian)]. Journal of Health and Care. 2011; 13(4):10-18.
Received: 2017/06/6 | Accepted: 2017/09/14 | Published: 2017/10/1
1. Chapman K, Arbon P. Are nurses ready. Australasian Emergency Nursing Journal. 2008; 11(3):135–44. doi: 10.1016/j.aenj.2008.04.002 [DOI:10.1016/j.aenj.2008.04.002]
2. Daneshmandi M, Amiri HO, Vahedi MA, Farshi MA, Saghafi A, Zigheymat F. Assessing the level of preparedness for confronting crisis such as flood, earthquake, fire and storm in some selected hospitals of Iran. Military Medicine Journal. 2010; 12(3):167-71.
3. Haji zadeh M, Seyyed Mazhari M, Pishgooie A, Aliyari S. [The effect of buddy aid education packages on the wives of military personnel awareness and attitude in the face with earth quake in 2013 (Persian)]. Military Caring Sciences. 2014; 1(1):1–8. doi: 10.18869/acadpub.mcs.1.1.1 [DOI:10.18869/acadpub.mcs.1.1.1]
4. Seyedin H, Abbasi Dolatabadi Z, Rajabifard F. Emergency nurses' requirements for disaster preparedness. Trauma Monthly. 2015; 20(4). doi: 10.5812/traumamon.29033 [DOI:10.5812/traumamon.29033]
5. Nasiripour AA, Raeissi P, Mahboubi M. Assess preparedness of border hospitals to deal with the borders crisis in Kermanshah province. Quarterly Journal of Health Administration. 2007;10(28):41-8.
6. Asl Hashemi A. Healh Actions in Emergencies (1). Tabriz: Tabriz University of Medical Sciences; 1995.
7. Asl Hashemi A. [The course of providing emergency health measures in increasing students' knowledge and attitudes (Persian)]. Journal of Medical Education Development. 2009; 1(1):9-15.
8. Dargahi A, Atafar Z, Karami A, Hoseini Ahagh MM, Zaebi Y, Amirian F, et al. knowledge and attitudes of Kermanshah University of medical sciences students in the field of food hygiene and safety. International Journal of Pharmacy & Technology. 2016; 8(2):13877-13890
9. Farrokhi M, Dolatabadi ZA, Pakjouei S, Pouyesh V. Approaches to post-disaster environmental recovery. Health in Emergencies & Disasters Quarterly. 2016; 1(2):65-70.
10. World Health Organization. Environmental health in emergencies. Geneva: World Health Organization; 2007.
11. Becker SM. Environmental disaster education at the university level: An integrative approach. Safety Science. 2000; 35(1-3):95–104. doi: 10.1016/s0925-7535(00)00025-4 [DOI:10.1016/S0925-7535(00)00025-4]
12. Abu Mourad T. Palestinian refugee conditions associated with intestinal parasites and diarrhoea: Nuseirat refugee camp as a case study. Public Health. 2004; 118(2):131–42. doi: 10.1016/j.puhe.2003.09.002 [DOI:10.1016/j.puhe.2003.09.002]
13. Jourvand R, Sadeghirad K, Golami OA, Vejdani M, Panahi R, Heydarabadi AB. Disasters preparedness of health workers in Dehloran, Iran. Journal of Health in the Field. 2017; 3(3):13-18.
14. Vosoughi Nayyeri M, Asgari M, Jahed Khaniki GH, Dargahi Gh, Golestani far H, Parastar S. [Investigation of knowledge and attitudes of students in Tehran University of Medical Sciences on health actions in emergencies (Persian)]. Quarterly Scientific Research Journal of Rescue & Relief. 2012; 4(2):43-51.
15. Vosoughi Niri A, Vosoughi Niri M, Golestanifar H, Pahlovanzadeh B, Savadpoor MT. Investigation of army soldiers readiness about health actions in critical circumstances, Case study: Malek Ashtar Military Garrison's. Health in Emergencies and Disasters. 2013; 1(1).
16. Biglari H, Hami M, Yari A, Poursadeghiyan M, Farrokhi M. Awareness of medical students of Gonabad University about nutrition and food preservation in disasters. Health in Emergencies and Disasters Quarterly. 2017; 2(3): 133-8. doi: 10.18869/nrip.hdq.2.3.133 [DOI:10.18869/nrip.hdq.2.3.133]
17. Rezaee Ashtiyani AA, Hadi MA. [Details of Arak citizens in dealing with in the event of natural disasters (Persian). Paper presented at the 6th National Congress on Environmental Health. 22-24 October 2003; Sari, Iran.
18. Khoshnazar T, Moghadamnia M, Khankah H, Ahmadi S. Awarencess of nursing students toward disaster preparedness in the health care system. Paper presented at the 5th International Congress on Health and Crisis Management in Disasters, 24-26 January 2011, Tehran, Iran.
19. Imani E, Hosseini Teshnizi S, Tafrihi M, Alavi A, Jafari A, Badri Sh, et al. [Nurses' knowledge about crisis management and its related factors (Persian)]. Journal of Health and Care. 2011; 13(4):10-18.