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Background noise lowers the performance of anaesthesiology residents’ clinical reasoning when measured by script concordance

A randomised crossover volunteer study

Enser, Maya; Moriceau, Jérôme; Abily, Julien; Damm, Cédric; Occhiali, Emilie; Besnier, Emmanuel; Clavier, Thomas; Lefevre-Scelles, Antoine; Dureuil, Bertrand; Compère, Vincent

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European Journal of Anaesthesiology: July 2017 - Volume 34 - Issue 7 - p 464-470
doi: 10.1097/EJA.0000000000000624
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Noise is omnipresent in hospitals and more particularly in operating rooms and ICUs. The WHO recommends that sound levels should not exceed 35 A-weighted decibels (dBA) for equivalent continuous sound level (LAeq; equivalent continuous sound pressure level measured over a period of time t, corresponding to the average sound level during this period of time; it is the main unit used for assessing occupational noise) in patient areas and the maximum level with A-weighted frequency response and fast time constant (maximum noise level measured over a given period of time) should remain below 40 dBA at night in hospitals.1 European directives for workers also define different daily noise exposure limits: the lower exposure action value is 80 dBA, and the upper exposure action value is 85 dBA; noise should not exceed 87 dBA.2 However, average sound levels, reported in several studies, vary from 56 to 71 dBA in operating rooms,3,4 and 52 to 59 dBA in ICU,5–7 with peak levels above 100 dBA.

Study of the negative effect of noise on patients and also on teams in the operating room and in the ICU has shown that it induces stress, discomfort and lack of sleep.8,9 An experiment with rats has indicated that noise might even be responsible for increased pain perception but, to date, there are no human studies.10 The most commonly reported health consequence on clinical staff exposed to high levels of noise is hearing loss.11 Noise may also negatively affect the clinical performances of staff through impaired communication, concentration and short-term memory.12 Stevenson et al.13 have shown that noise can interfere with perception of the pulse oximeter and lessen the anesthesiologist's ability to detect a reduction in oxygen saturation.

To date, there are no studies on the effect of noise on clinical reasoning. One tool used to evaluate clinical reasoning in conditions of uncertainty is the script concordance test (SCT).14 Its format captures the uncertainty that characterises medical practice, and physicians consider that the required cognitive tasks are closer to the reality of clinical reasoning than in other kind of tests.15,16 This methodology has also been successfully used for medical education including anaesthesiology, to assess the impact of tutored practice exchange groups on medical reasoning,17,18 and to discriminate between anaesthesia providers with varying levels of experience in anaesthesiology.19 To our knowledge, it has not yet been used in an anaesthesiology context when residents are confronted with high levels of noise.

The primary objective of the study was to assess the impact of noise on anaesthesiology residents’ clinical reasoning when measured by SCT. The secondary objectives were to assess the concentration of the residents in both noisy and quiet environments, and to compare SCT scores between different groups of students, in particular between junior and senior residents.

Material and methods

The Ethics and Evaluation Committee for Non-Interventional Research based at Rouen University Hospital approved the study in March 2014 (No. E2014-03). All participants received information before any study procedures were undertaken and provided informed verbal consent. The requirement for written informed consent was waived by the Institutional Review Board. The test was anonymous. The mean noise level did not exceed the limit of 87 dBA, as recommended by European directives on minimum health and safety requirements for the exposure of workers to risks arising from physical agents (noise).2

Study design and resident selection

The current prospective, controlled, randomised and crossover study was conducted at Rouen University Hospital. All anaesthesiology residents enrolled in the training programme were included. Learning objectives for anaesthesiology residents are set out in the French National Guidelines for anaesthesia teaching, given to all participating residents at the beginning of their 5-year anaesthesiology programme. The training programme for all residents includes several types of learning with 10 days dedicated to lectures on specific topics during the academic year. In addition, a weekly journal club and a weekly practice exchange group are organised. The university department also provides at least two training sessions on high-fidelity simulation during the 5-year programme.

Study procedures

Script concordance test

The SCT was intended to confront residents with authentic clinical situations which were described in vignettes. The clinical situations were problematic even for experienced clinicians, either because data were lacking or because situations were ambiguous. There were several options for diagnosis, investigation or management. The items (questions) were based on a panel of questions that an experienced clinician (anaesthesiologist) considered relevant to the type of clinical settings that might confront an anaesthesiologist (Table 1). The items were consistent with the presentation of relevant options and new data (not described in the vignette). The task for the resident consisted in determining the effect this new data had on the status of the option. An example of items from the therapeutic section of the test is illustrated in Table 1.

Table 1
Table 1:
Example of script concordance test applied to the case of a 20-year-old female undergoing a laparoscopic appendectomy for whom anaesthesia must be induced

The SCT was developed following guidelines described by Fournier et al.20 and Dory et al.,21 and an independent reviewer (LS) facilitated in its development. The SCT initially included 90 vignettes that described the clinical situations that might confront anaesthesiology or intensive care residents. Each vignette included three items. Four authors assumed responsibility for developing items and ensuring clarity and relevance for the SCT. The first item included a diagnostic hypothesis, a plan for investigation or a management recommendation. Then new information, a symptom, a sign or a result of an investigation, was presented. The resident's task was to assess, using a 5-point Likert scale, the influence of this new element on the diagnostic hypothesis, or the plan for investigation or management. The different points of the scale corresponded to positive values (+2 – the option was enhanced strongly, and +1 – relatively, by the new data), neutral values (the data did not change the status of the option) or negative values (−2 – this option was ruled out strongly and −1 relatively by the data).

The resident's response to each question was compared with that of the members of a reference panel of experts (experienced clinicians). According to Dory et al.,21 10 to 20 anaesthesiologists should make up the expert panel. In our previous studies,17,18 17 experts were involved for 1 h in a quiet environment. The scoring system was based on the principle that any answer given by any one expert had an intrinsic value, even if that answer did not coincide with that of the other experts. For each item, the answer entitled the resident to a credit corresponding to the number of experts who had chosen it. All items had the same maximum credit (1 point), and raw scores were transformed proportionally to obtain the maximum credit of 1 point for the answer that was chosen by most experts. Other choices received a partial credit (from 0 to 0.99999). Thus, to calculate the scores, all results were divided by the number of experts who had given the most frequently chosen answer. For example, for a panel of 10 experts, if five experts selected response +1, four experts selected response +2, and one expert selected response 0, the score for this question would be response 0, 0.2 points (1/5); response +1, 1 point (5/5); response +2, 0.8 points (4/5); responses 1 and 2, both 0 points.22

The total score for the test was the sum of all credits earned for each item. The total score was then transformed into a percentage score. Automatic correction software was used for scoring (available at; September 2009).

All study participants had the same exposure to SCT since this assessment methodology had only been recently introduced to our centre and is not yet widely used for resident evaluation.

Residents assessment

Two resident groups were formed with randomisation using Daktari software, Quimper, France. The assessment consisted of 56 SCT over 1 h in the same day.

As a crossover design, each group was exposed to both quiet and noisy atmospheres, while taking the SCT assessment. Group A did the first part of the assessment (28 SCT) in a quiet environment and the second part (28 SCT) in a noisy one. Group B did the same in reverse order.

To evaluate the experience factor on the impact of noise on clinical reasoning, the SCT scores of ‘junior’ (years 1 and 2 of training) and ‘senior’ (years 3 and 4 of training) residents were compared.

Noisy environment

The noisy part of the test recreated the background noise of real emergency situations. We made a sound track with different French audio extracts of television medical dramas (Emergency Room, Grey's anatomy) and extracts of noise directly recorded in our operating room. This was used for the noisy part, with background sounds of voices, cardiac monitor alarms, the sound of equipment falling and more, whilst residents took the SCT. For the quiet part of the test, the background noise was simply that of the examination room, with rustling paper, coughing and external street noise. The source of the noise was four loudspeakers placed to equilibrate the sounds for every resident. We tested the sound homogeneity with receptors in different places in the room.

Noise level was measured using a sound meter. The device used was an iphone application: Decibel Ultra 2.2, Berlin, Germany. Iphone technology uses micro-electromechanical system microphones that can capture signals as low as 30 dB and as high as 120 to 130 dB. Although the Decibel Ultra 2.2 application has not been especially evaluated, it has been shown that most sound measurement applications for Apple smartphones may be considered accurate and reliable enough to be used to assess occupational noise exposures.24 The equivalent continuous sound pressure level measured over a period of time – t (LAeq) was measured in the noisy and quiet parts of the test-period.

Study outcomes

The primary outcome was the resident's performance as measured by the SCT score, with and without noise. The performance in SCT was expressed as the degree of concordance with the panel of 17 experts (expressed out of 100 points). The assessor was blinded to group assignments.

Secondary outcomes included

  1. Comparison of overall SCT scores between the two groups
  2. Comparison of SCT scores between the different resident years
  3. Auto-evaluation of residents’ concentration in noisy and quiet environments by visual analogue scale (VAS). The scale was a horizontal line graduated from 0 (no concentration at all) to 10 (best concentration you can imagine). The resident circled the number corresponding to their estimated degree of concentration.

Data included information on the residents’ personal characteristics.

Statistical analysis

We reported that the SCT score (without noise) for residents enrolled in the anaesthesiology training programme at Rouen University Hospital was 64/100 [confidence interval (CI) 95%: 62 to 66].17 To calculate the sample size, we assumed that a difference of 6% for SCT test (64 for the quiet group and 60.2 for the noisy) between the two groups would be significant (a difference at least of 3% between two groups would be significant for the SCT).24 Based on these findings and assuming that the CI was the same between the groups and using a power of 0.90 with a level of statistical significance at 0.05, it was estimated that 39 residents should be included in each group (i.e. a total of 39 residents, because of the crossover design). Considering the likelihood of dropouts, we enrolled 43 residents.

The Shapiro–Wilk test was used to test for normal distribution of variables. Comparison between normally distributed variables was conducted using a parametric one-tailed t-test. Variables that were not normally distributed were analysed using the Mann and Whitney test. Results which were normally distributed were expressed as means, with 95% CI. Quantitative variables which were not normally distributed were reported as median (interquartile range). Statistical analysis was performed using Prism 6.01 for windows, and the Paris VI University web site:



Fifty-four residents were eligible for the study. Eleven residents failed to show up at the time of the SCT exam, leaving a total of 43 residents for inclusion. Mean (extreme) age is 28.2 (26 to 35) years, and the sex ratio is 22 women and 21 men. A flow chart is presented in Fig. 1. After randomisation, two groups of 23 and 20 residents were formed. Distribution by year of residency is presented in Table 2. The analysis of the SCT score included only 42 residents because one resident initially included did not complete his year of training on the data collection sheet.

Fig. 1
Fig. 1:
Flow chart.
Table 2
Table 2:
Distribution by year of residency

Noise levels

In the noisy environment, LAeq was 63.9 dBA, with peaks of 85.9 dBA and minimum sound level of 50.9 dBA. In the quiet atmosphere, LAeq was 53.4 dBA, with peaks of 77.7 dBA and a minimum sound level of 40.2 dBA.

Script concordance tests scores

All variables were normally distributed (all P values were >0.05 for the Shapiro–Wilk test). The SCT was optimised by post-hoc analysis as described by Lubarsky et al. Items with high variability, low variability or binomial responses were excluded. After this optimisation, a final version with 56 items was obtained, and Cronbach's alpha coefficient (index of consistency and reliability) was 0.82. The test is considered to be sufficiently reliable when Cronbach's alpha coefficient reaches a value of 0.80.23

The overall performance in the SCT expressed as degree of concordance with the expert panel (95% CI) was 60.9 (59.1 to 62.8). There was no difference between Group A and Group B scores [60.9 (57.7 to 64.1) and 61.0 (59.3 to 62.6), P = 0.96]. Performance in the SCT expressed as degree of concordance with the expert panel (95% CI) was higher in the quiet group than in the noisy group [62.8 (60.8 to 64.9) vs 59.0 (56.0 to 62.0), P = 0.04]. Overall performance in the SCT expressed as degree of concordance with the expert panel (95% CI) was higher for year 3 and 4 than for year 1 and 2 residents [63.4 (61.2 to 65.6) vs 58.2 (55.6 to 60.8), P = 0.004]. The difference in the SCT score between quiet [61.5 (57.9 to 65.1)] and noisy environments [54.8 (50.6 to 59.1)] was greater for year 1 and 2 residents and, inversely, was less (not statically significant) for year 3 and 4 residents [64.0 (61.9 to 66.1) for the quiet and 62.9 (59.2 to 66.5) for the noisy environment]. All results are presented in Table 3.

Table 3
Table 3:
Script concordance test scores according to year of training

Auto-evaluation of residents’ concentration

Variables were not normally distributed (P value was <0.05). Residents’ concentration was auto-evaluated using a VAS. In the quiet environment, the median (25 to 75%) residents’ concentration VAS was 8 (7 to 8) and in the noisy environment, the median (25 to 75%) residents’ concentration VAS was 3 (2.5 to 5) (P < 0.0001). There was no difference in concentration level between the groups of residents in years 1 to 2 and in years 3 to 4, nor between quiet and noisy environments (P values = 0.08 and 0.76, respectively).


Residents’ performance, measured by the SCT, was significantly poorer in a noisy environment than in a quiet environment. The noise affected year 1 to 2 residents more than year 3 to 4 residents. The difference in the SCT score between quiet and noisy environments was not seen in year 3 to 4 residents. Also, the residents’ concentration, measured by auto-evaluation using a VAS, was more than 50% lower in the noisy environment compared with the quiet environment (P < 0.001), with no difference between the groups of residents in years 1 to 2 compared with years 3 to 4 (P > 0.05).

The SCT is based on clinical situations that are described in vignettes that raise questions regarding diagnosis, investigation or management. The SCT has previously been shown to be a reliable and valid measure of clinical reasoning in several medical disciplines.23–27 It is increasingly used in various medical fields: the training assessment of medical students, residents’ training and continuing medical education.28–30 The scoring system is based on the principle that each interpretation of clinical data can be compared with that of a panel of experts, therefore providing a measure of clinical reasoning quality, taking into account the variability of reasoning processes among experts (unlike Multiple Choice Questions where there is only one single best answer). However, the SCT only assesses that part of clinical skills to do with clinical reasoning and data interpretation and does not assess medical knowledge as precisely as other tests.14 The SCT is intended to evaluate poorly defined problem solving ability in complex cases or in uncertain conditions. In this context, the test has been validated in the assessment of residents in different medical fields that include radiology and surgery.31–33. Several studies describe the use of the SCT in anaesthesiology17–19; here it seems particularly suitable where speedy decision-making in conditions of uncertainty is part of everyday life.

Nouh et al.24 have written recently that a difference of 3% for the SCT between two groups was of sufficient magnitude to be considered important. This highlights the negative effect of noise on our residents’ performance as measured by the SCT. Several studies have considered the effect of noise on various medical skills, but to our knowledge ours is the first to suggest that noise has a direct impact of on clinical reasoning in uncertain situations. Several previous studies have examined the impact of noise on concentration. Park et al.34 studying radiology residents showed that their ability to diagnose rib fractures was poorer in a noisy environment, something they were not accustomed to. In this study, eight radiology residents sought rib fracture(s) in the chest radiographs of 110 patients in both quiet and noisy conditions. The radiologists were divided into two groups: those accustomed to a quiet environment in group 1, and in group 2, those accustomed to or unaffected by noise. Group 1 performed better in quiet conditions than in noisy conditions, and the opposite tendency was observed for group 2.

Noise can widely affect communication between the various personnel in an operating room and should be considered as a risk factor for medical error. Consequently it should be limited as much as possible. However, currently there are no specific recommendations for noise in our workplaces, contrary to the state of affairs in the aeronautics sector where, to limit the risk of accidents caused by inattention, there are very strict rules, particularly relating to the critical phases of take-off and landing. These are the ‘sterile cockpit rules’, the content of which stipulates: ‘Activities such as […] engaging in nonessential conversations within the cockpit and nonessential communications between the cabin and cockpit crews, […] are not required for the safe operation of the aircraft’.35 It would seem necessary to apply these recommendations to operating rooms during critical phases such as induction of anaesthesia, reversal and resuscitation.

Noise can also affect clinical performance. It has been shown that negative acoustic stimuli such as dichotic music (different music in each ear) can affect concentration, performance and speed not only in experts but also in novice surgeons.36 Feuerbacher et al.37 have shown an important increase in the chance of error (damage to arteries, bile duct etc.) in novice surgeons working on simulators, when subject to distraction from talking and noise in general, compared with those working in quiet surroundings. In our study, this was highlighted by the significant drop in residents’ concentration when exposed to noise. Stevenson et al.13 found that noise in the operating room affected the ability to detect changes in oxygen saturation conveyed by auditory cues, increasing the chance of an error. Our study corroborates this in showing the negative effect of noise on clinical reasoning in anaesthesiology residents. Further evidence for this can be found in the report of two cognitive tests, showing that noise affected anaesthesiology residents’ mental efficiency and short-term memory.38

In our study, the impact of noise seemed to decrease proportionately with the year of training. This has support from a virtual laparoscopic study that showed the negative effect of music on surgical performance in particular on the first exposure.39 With experience, this effect disappeared completely, suggesting that the brain adapts to noise. Our year 3 to 4 anaesthesiology residents were widely exposed to acoustic disturbances in the operating room, explaining the lower impact of noise on their performance. In the same way, Hawksworth et al.40 showed that when experienced anaesthesiologists (mean age 30 years) were exposed to music, their psychomotor performance was the same as in a quiet environment.

One limitation of our study was the setting of the sound level used for both the quiet and the noisy environments. The mean sound level in operating rooms is reported to vary from 60 to 85 dBA.12 Kracht et al.3 found that sound peaks were above 100 dBA, for more than 40% of the time for certain types of surgery, with regular peaks up to 120 dBA. To protect the residents’ hearing, the sound levels, both mean and peaks, used in our study were a little lower than that. Recently McNeer et al.41 found that a difference of 5 dBA between noisy and quiet conditions increased the feeling of stress in anaesthesiology residents. The difference between quiet and noisy environments in our study, at (10 dBA), was greater and so we may have underestimated the difference between residents’ performance in quiet and noise. Another limitation was that we had not asked the residents about their previous exposure to workplace noise. We have already found this to be a key factor in the impact of noise on clinical performance.34 Finally, there was a design bias because the patients were not blinded and were aware of the quiet or noisy nature of their environment. There was no observer bias because of the notation system of the SCT, which used a Lickert scale converted into points according to the experts’ answers.

In summary, we have shown that noise alters the clinical reasoning of anaesthesiology residents as measured by the SCT. The impact was greater for residents beginning their training. This observation adds to what is already well known about the adverse effects of noise on medical and paramedical staff. The implementation of awareness campaigns to limit noise in operating rooms appears imperative, especially when junior residents are in charge of the patient. Inspired in particular by the aeronautics sector, the adoption of ‘sterile cockpit rules’ to eradicate nonessential communication during critical phases, could readily be introduced. In this context, the use of an emergency manual that includes cognitive aid cards has promise in limiting talk to the essentials and maintaining a quiet environment. Another approach would be to acclimatise residents to workplace noise to prevent impairment of their clinical reasoning in this context. The silent operating room is not a realistic concept. Even if the sound level in operating rooms could be reduced, it would still make sense for anaesthesiologists to adapt to noisy conditions to be able to function in the invariably loud environment of certain emergency situations such as major trauma.

Acknowledgements relating to this article

Assistance with the study: the authors are grateful to Nikki Sabourin-Gibbs, Rouen University Hospital, for writing assistance and review of the article in English.

Financial support and sponsorship: none.

Conflicts of interest: none.

Presentation: preliminary data for this study were presented as a poster presentation at the French Society of Anaesthesiology and Critical Care (SFAR), 17–19 September 2015, Paris.


1. Berglund B, Lindvall T, Schwela DH. Guidelines for community noise. Geneva: OMS; 1999.
2. Directive 2003/10/EC of the European Parliament and the Council of 6 February 2003 on the minimum health and safety requirement regarding the exposure of workers to the risks arising from physical agents (noise) (seventeenth individual directive within the meaning of article 1651) of directive 89/391/CEE). 2003 [Accessed 5 January 2017].
3. Kracht JM, Busch-Vishniac IJ, West JE. Noise in the operating rooms of Johns Hopkins Hospital. J Acoust Soc Am 2007; 121:2673–2680.
4. Ginsberg SH, Pantin E, Kraidin J, et al. Noise levels in modern operating rooms during surgery. J Cardiothorac Vasc Anesth 2013; 27:528–530.
5. Christensen M. Noise levels in a general intensive care unit: a descriptive study. Nurs Crit Care 2007; 12:188–197.
6. Darbyshire JL, Young JD. An investigation of sound levels on intensive care units with reference to the WHO guidelines. Crit Care 2013; 17:R187.
7. Elliott R, Rai T, McKinley S. Factors affecting sleep in the critically ill: an observational study. J Crit Care 2014; 29:859–863.
8. Elliott RM, McKinley SM, Eager D. A pilot study of sound levels in an Australian adult general intensive care unit. Noise Health 2010; 12:26–36.
9. Hasfeldt D, Laerkner E, Birkelund R. Noise in the operating room—what do we know? A review of the literature. J Perianesth Nurs 2010; 25:380–386.
10. Shankar N, Awasthy N, Mago H, et al. Analgesic effect of environmental noise: a possible stress response in rats. Indian J Physiol Pharmacol 1999; 43:337–346.
11. Wallace MS, Ashman MN, Matjasko MJ. Hearing acuity of anesthesiologists and alarm detection. Anesthesiology 1994; 81:13–28.
12. Katz JD. Noise in the operating room. Anesthesiology 2014; 121:894–898.
13. Stevenson RA, Schlesinger JJ, Wallace MT. Effects of divided attention and operating room noise on perception of pulse oximeter pitch changes: a laboratory study. Anesthesiology 2013; 118:376–381.
14. Lubarsky S, Dory V, Duggan P, et al. Script concordance testing: from theory to practice: AMEE guide no. 75. Med Teach 2013; 35:184–193.
15. Charlin B, Roy L, Brailovsky C, et al. The Script Concordance test: a tool to assess the reflective clinician. Teach Learn Med 2000; 12:189–195.
16. Charlin B, van der Vleuten C. Standardized assessment of reasoning in contexts of uncertainty: the script concordance approach. Eval Health Prof 2004; 27:304–319.
17. Compère V, Abily J, Moriceau J, et al. Residents in tutored practice exchange groups have better medical reasoning as measured by script concordance test: a controlled, nonrandomized study. J Clin Anesth 2016; 32:236–241.
18. Compère V, Moriceau J, Gouin A, et al. Residents in tutored practice exchange groups have better medical reasoning as measured by the script concordance test: a pilot study. Anaesth Crit Care Pain Med 2015; 34:17–21.
19. Ducos G, Lejus C, Sztark F, et al. The Script Concordance Test in anesthesiology: validation of a new tool for assessing clinical reasoning. Anaesth Crit Care Pain Med 2015; 34:11–15.
20. Fournier JP, Demeester A, Charlin B. Script concordance tests: guidelines for construction. BMC Med Inform Decis Mak 2008; 8:18.
21. Dory V, Gagnon R, Vanpee D, et al. How to construct and implement script concordance tests: insights from a systematic review. Med Educ 2012; 46:552–563.
22. Lubarsky S, Charlin B, Cook DA, et al. Script concordance testing: a review of published validity evidence: validity evidence for script concordance tests. Med Educ 2011; 45:329–338.
23. Charlin B, Gagnon R, Sibert L, et al. Le test de concordance de script, un instrument d’évaluation du raisonnement clinique. Pédagogie médicale 2002; 3:135–144.
24. Nouh T, Boutros M, Gagnon R, et al. The script concordance test as a measure of clinical reasoning: a national validation study. Am J Surg 2012; 203:530–534.
25. Humbert AJ, Miech EJ. Measuring gains in the clinical reasoning of medical students: longitudinal results from a school-wide script concordance test. Acad Med 2014; 89:1046–1050.
26. Gibot S, Bollaert P-E. Le test de concordance de script comme outil d’évaluation formative en réanimation médicale. Pédagogie médicale 2008; 9:7–18.
27. Lubarsky S, Chalk C, Kazitani D, et al. The Script Concordance Test: a new tool assessing clinical judgement in neurology. Can J Neurol Sci 2009; 36:326–331.
28. Duggan P, Charlin B. Summative assessment of 5th year medical students’ clinical reasoning by Script Concordance Test: requirements and challenges. BMC Med Educ 2012; 12:29.
29. Hornos EH, Pleguezuelos EM, Brailovsky CA, et al. The practicum script concordance test: an online continuing professional development format to foster reflection on clinical practice. J Contin Educ Health Prof 2013; 33:59–66.
30. Labelle M, Gagnon RJ, Thiverge RL, et al. Formation continue en petits groupes sur l’ ostéoporose: comparaison d’un atelier basé sur le test de concordance de scripts (TCS) et d’un atelier classique. Pédagogie médicale 2003; 4:145–153.
31. Sibert L, Darmoni SJ, Dahamna B, et al. On line clinical reasoning assessment with Script Concordance test in urology: results of a French pilot study. BMC Med Educ 2006; 6:45.
32. Kania RE, Verillaud B, Tran H, et al. Online script concordance test for clinical reasoning assessment in otorhinolaryngology: the association between performance and clinical experience. Arch Otolaryngol Head Neck Surg 2011; 137:751–755.
33. Lambert C, Gagnon R, Nguyen D, et al. The script concordance test in radiation oncology: validation study of a new tool to assess clinical reasoning. Radiat Oncol 2009; 4:7.
34. Park SH, Song HH, Han JH, et al. Effect of noise on the detection of rib fractures by residents. Invest Radiol 1994; 29:54–58.
35. Broom MA, Capek AL, Carachi P, et al. Critical phase distractions in anaesthesia and the sterile cockpit concept. Anaesthesia 2011; 66:175–179.
36. Conrad C, Konuk Y, Werner PD, et al. A quality improvement study on avoidable stressors and countermeasures affecting surgical motor performance and learning. Ann Surg 2012; 255:1190–1194.
37. Feuerbacher RL, Funk KH, Spight DH, et al. Realistic distractions and interruptions that impair simulated surgical performance by novice surgeons. Arch Surg 2012; 147:1026–1030.
38. Murthy VS, Malhotra SK, Bala I, et al. Detrimental effects of noise on anaesthetists. Can J Anaesth 1995; 42:608–611.
39. Miskovic D, Rosenthal R, Zingg U, et al. Randomized controlled trial investigating the effect of music on the virtual reality laparoscopic learning performance of novice surgeons. Surg Endosc 2008; 22:2416–2420.
40. Hawksworth CR, Sivalingam P, Asbury AJ. The effect of music on anaesthetists’ psychomotor performance. Anaesthesia 1998; 53:195–197.
41. McNeer RR, Bennett CL, Dudaryk R. Intraoperative noise increases perceived task load and fatigue in anesthesiology residents: a simulation-based study. Anesth Analg 2016; 122:512–525.
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