Conflict of Interest Declaration
The ILCOR Continuous Evidence Evaluation process is guided by a rigorous ILCOR Conflict of Interest policy. The following Task Force members and other authors declared an intellectual conflict of interest, and this was acknowledged and managed by the Task Force Chairs and Conflict of Interest committees: none
Task Force Synthesis Citation
Chih-Wei Yang, Cheng-Heng Liu, Andrew Lockey, Adam Cheng, Robert Greif on behalf of the International Liaison Committee on Resuscitation Education, Implementation and Teams Task Force. Workload and Stress during Resuscitation. [Internet] Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Education, implementation, Team Task Force, 2023 July 5. Available from: http://ilcor.org
Methodological Preamble
The continuous evidence evaluation process started with a scoping review of ‘workload and stress during resuscitation’ conducted by the ILCOR EIT Task Force and one external content expert (CH Liu). The literature search was conducted by an Information Specialist from ILCOR (Mary-Doug Wright) with involvement of a clinical content expert (CW Yang). All titles were screened by pairs of researchers solving disagreements by discussion or by involving a third group member. The final scoping review’s task force insight was discussed and agreed during EIT Task Force meetings and approved by the ILCOR Science Advisory Committee.
Scoping Review
Webmaster to insert the Scoping Review citation and link to Pubmed using this format when/if it is available. Note: This should reflect the Scoping Review Contributing Authors
PECOST
Population: Healthcare providers performing resuscitation on patients in cardiac arrest in clinical or on manikins in a simulated setting
Exposure: Presence of any factors that would possibly impact the healthcare provider’s perceived workload or stress
Comparison: Absence of the specific factor
Outcomes: Objective or subjective measures of workload and/or stress experienced by healthcare providers during resuscitations.
Study Designs: Randomized controlled trials (RCTs) and non-randomized studies (non-randomized controlled trials, interrupted time series, controlled before-and-after studies, cohort studies), unpublished studies (e.g., conference abstracts, trial protocols), letters, editorials, comments, case reports, grey literature and social media are eligible for inclusion. All relevant publications in any language are included as long as there is an English abstract.
Timeframe: All years and all languages are included as long as there is an English abstract
Literature search updated to 2023 April 21.
The question the Task Force EIT was interested was: “Amongst healthcare providers, what variables influence (i.e., increase or decrease) provider workload and/or stress during cardiac arrest, in both real-world and simulated scenarios.”
Inclusion criteria:
- Studies that investigate workload and stress during resuscitation on patients with cardiac arrest among healthcare providers (e.g., physicians, nurses, paramedics, etc.), either in real-world or simulated scenarios.
- Studies that identify and describe variables that influence workload and stress during management of cardiac arrest.
Exclusion criteria:
- Studies that do not report on workload or stress during cardiac arrest
- Studies that only report on workload or stress during non-cardiac arrest resuscitation scenarios (e.g., trauma resuscitation)
- Studies that describe educational interventions aimed at reducing workload or stress during cardiac arrest.
- Studies that examine the effect of patient status (survival or mortality) on the stress and/or workload experienced by resuscitation teams.
Outcomes for extraction
- Objective and subjective measures of workload, including self-reported questionnaires such as NASA Task Load Index (NASA-TLX), Subjective Workload Assessment Technique (SWAT), etc.
- Objective and subjective measures of stress, including physiologic measures (heart rate, cortisol, salivary amylase, blood pressure), validated scales such as State Trait Anxiety Inventory, visual analogue scale for anxiety (VAS-A), etc.
- Any variables identified to have impact on workload and/or stress: examples include family presence, mechanical chest compression device, size of resuscitation team, etc.
Definitions:
Workload refers to the cognitive, physical, and emotional demands placed on an individual while performing a task or a set of tasks. It encompasses various dimensions such as mental workload, which includes attention, memory, and decision-making demands, as well as physical workload, which involves the degree of physical effort, force, and dexterity required to complete a task. Excessive workload can lead to stress, fatigue, and decreased performance, particularly in high-stakes environments like healthcare settings where precision and efficiency are crucial.[1]
One tool used to measure workload is the NASA Task Load Index (NASA-TLX). The NASA-TLX was developed by the National Aeronautics and Space Administration (NASA) to measure the workload of pilots and has since been used in various fields including healthcare. The NASA-TLX uses a multidimensional construct to derive an overall workload score based on a weighted average of ratings on six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration level.[1]
Stress refers to the psychological and physiological response of an individual when facing demanding situations or challenges that exceed their perceived ability to cope. High-stress environments, such as emergency medical situations, can affect the performance of healthcare providers by impairing their decision-making, communication, and overall effectiveness.[2]
Stress markers included physiologic markers, such as salivary cortisol and amylase, heart rate, heart rate variability and blood pressure. Subjective scales included The State-Trait Anxiety Inventory, a widely used questionnaire with supportive validity evidence that measures stress as a trait [1]. The STAI was designed to assess cognitive and somatic symptoms of anxiety as they pertain to one's mood in the moment (state) and in general (trait)[3].
Task Force Insights
1. Why this topic was reviewed?
The performance of cardiopulmonary resuscitation (CPR) for cardiac arrest patients is an essential and challenging responsibility for healthcare professionals, requiring them to perform specific tasks with precision and accuracy while concurrently working efficiently as a team to following clinical care guidelines. The workload and stress healthcare providers might experience during resuscitation have the potential to affect the performance of individual rescuers or the resuscitation team [4,5]. Unfortunately, it is not clear what factors increase or decrease workload or stress experienced by individual resuscitation team members, or how these factors affect the overall performance of the team during CPR. We thus consider factors influencing healthcare providers’ workload or stress the question of interest for this scoping review. This topic was chosen because there is emerging literature investigating workload and stress during resuscitation performance, either on real or simulated patients [6]. There has also been considerable focus placed on studying and teaching resuscitation providers about certain human factors, such as teamwork, and how to mitigate their effects on performance [7]. This scoping review will focus on potential variables influencing healthcare provider’s perceived workload or stress, when performing resuscitation on patient in cardiac arrest or in simulated settings. Once influencing factors are identified and defined, mitigating strategies can be developed accordingly.
2. Narrative summary of evidence identified
We identified 9570 studies through the database search including Medline, EMBASE, PsycINFO, Cochrane, and Allied Health Literature (CINAHL). A total of 7210 title and abstract were screened after removal of duplicates, which excluded another 7124, leaving 86 for full-text review. After full text review, 22 articles were included for final data extraction [8–29]. (Figure.1)
Study Characteristics
The included 22 studies are heterogeneous in study design. Eighteen are RCTs [9,10,12–19,21–24,27–29], two are experimental designs without randomization[11,26], and two are observational studies[8,25]. All but two studies[8,25] were done in a simulation-based setting, with seven using pediatric scenarios[9,13,19,21,26,27,30] and the remaining adult clinical scenarios.
Outcomes of interest for this review were extracted, as well as variables influencing individual healthcare provider’s workload and stress during resuscitation. Tools used were the following: the NASA Task Load Index [9,10,12,13,15,16,19,21–29,31] was the main method for measuring subjective workload, and the State-Trait Anxiety Inventory [18], visual analogue scale (VAS) [32], and structured survey questions [14] were used to measure stress. Physiologic markers used to measure stress included salivary cortisol and alpha-amylase levels [24] Heart Rate and Blood Pressure[11]. Variables that may have an influence on perceived stress or workload were extracted and categorized into the following separate entities: 1) team composition and roles such as designation of nursing team leader, CPR coaches or comparison of workloads between resuscitation team leaders and members; 2) Telemedicine which involves teams that are supervised or led remotely; 3) Workflows such as prioritization of CPR automation or task-focusing techniques; 4) Tools including CPR-feedback device; 5) Cognitive aids including manuals, and smart apps; 6) Presences of friends and families as socioemotional stress, 7) provider experience and exposure.
*CPR coaches: a designated person or role responsible for monitoring chest compression rate, depth and compression fractions, and is mandated to provide feedback if the CPR quality is suboptimal.
1. Team composition and roles
One included randomized controlled simulation study suggested that the presence of a nursing team leader can significantly alleviate the medical team leader's workload during emergency resuscitation[29]. In the study, the designated nursing team leader was asked to actively take on tasks including timing of 2-min cycles, rhythm checks, defibrillation if indicated, monitoring CPR qualities, and prompting drug administrations. On the other hand, the presence of a CPR coach can decrease mental workload but increase physical workload among CPR providers[23], whereas their presence did not impact workload for resuscitation team leaders [16,23].
In another prospective cohort study of real pediatric resuscitations, team leaders reported higher mental load, whereas chest compressors reported higher physical workload[8].(Table 1a.)
2. Telemedicine
Two of the included randomized controlled trials (RCTs) explored models of telemedicine. In a 2021 study, Butler et al. compared resuscitation teams led remotely to teams with an on-site leader. Their findings revealed that the group utilizing telemedicine experienced significantly higher overall workload and mental demand compared to the group receiving usual care[13]. Another study randomized resuscitation groups to remote team leaders
actively leading the resuscitation, to remote consultant providing guidance on request of team members. The workloads were significantly increased for resuscitation team members with teleconsultant only[27]. (Table 1b.)
3. Workflows
Adjustment of workflows, such as prioritizing chest compression automation with mechanical CPR device [28], or deliberate re-orientation with task-focusing questions[33], can reduce perceived workload and stress in simulation. (Table 1c.)
- Tools
One randomized controlled trial in simulation showed that the use of ventilation feedback device and chest compression feedback device both increase workload for CPR providers[9]. One another RCT investigating real-time feedback device found no significant effect on team leaders, while CPR providers doing chest compressions reported significantly higher workloads as well[34]] Interestingly, equipment failure (i.e. defective defibrillator) in a simulated cardiac arrest scenario did not result in increased stress for the resuscitation team [24]. (Table 1d.)
- Cognitive aids and smart apps
Cognitive aids featuring resuscitation protocols or decision support functions, presented in various formats such as mobile applications or paper manuals, have also been investigated in several studies[10,14,18,21,26]. A smart app designed to help pediatric drug preparation was effective in reducing acute stress in paramedics during simulated pediatric cardiac arrest scenarios[18], whereas one another smart app with a built-in resuscitation algorithm did not result in significant difference on team leaders’ workloads between teams with and without the app[26]. Charles et al., investigated the effects of team size and decision support tools on healthcare providers’ workload measured by the NASA-TLX. Teams of two participants had significantly higher average NASA-TLX scores than participants in teams of three[21]. For the effect of a tablet-based decision support tool on workload results are inconclusive, as a significantly increased workload was reported in the first simulation, which disappeared in the second simulation[21]. (Table 1e.)
- Family presence/Socioemotional stress
Three RCTs investigated the effect of family presence[15], agitated relatives[12] or upset friends[22] on healthcare provider’s workload during adult resuscitation in simulation settings. Presence of next of kin increased significantly mental demands but did not change physical demands. Although few studies reported measured stress, a systematic review done on family presence during adult resuscitation from cardiac arrest also support that family presence may increase provider’s perceived stress[35]. Nonetheless, the studies stated above utilized simulated setting and standardized family/friends that were intended to be obstructive and noxious, which may result in different effects on workload from real world situations. An observational study of real pediatric resuscitations, however, showed that total NASA-TLX score was lower when at least one parent was present.[25] This finding is compatible with the ILCOR Consensus of Science and Treatment Recommendation and systematic review on family presence during resuscitation in pediatric and neonatal cardiac arrest, that it is reasonable for mothers/fathers/partners to be present during the resuscitation of neonates where circumstances, facilities and parental inclination allow. [36][37]. (Table 1f.)
- Providers Experience
A quasi-experimental study found no association between level of clinical experience and subjective stress and physiologic parameters among nursing students during resuscitation simulation and found no difference[11]. (Table 1g.)
Narrative Reporting of the Task Force discussions
Based on the studies referenced above, we have identified multiple categories of factors that may influence healthcare providers’ perceived workload during resuscitation in cardiac arrest scenarios. The designated medical team leader tends to experience an increased workload, and this can be attenuated by assigning a senior nurse as nurse leader. However, studies investigating CPR coaches showed that the addition of a CPR coach does not significantly affect the team leader’s overall workload.
Positioning the team leader at a remote site can also increase the workload for the team. Conversely, adopting a goal-directed approach and/or utilizing task-focusing questions during resuscitations can potentially reduce the perceived workload or stress for the resuscitation team. Although external support such as cognitive aids, have shown neutral or positive effects toward reducing stress/workload, one study[21] demonstrated that teams firstly exposed to decision support tools may experience higher workloads. This finding suggests that introducing new equipment could potentially impose an additional cognitive burden if the users are not adequately familiarized with it.
Naturally, the identifiable factors isolated in this review, including team composition, roles, workflows, tools, telemedicine, cognitive aids, smart apps, and socioemotional stress, represent potential modifiable elements. Adjusting these factors could alleviate or increase their impact on workloads or stress, and consequently, on resuscitation performance as well. However, it’s important to note that there may be additional factors influencing the workload of resuscitation team members that were not covered in our review, due to the absence of direct measurements for these factors among identified studies[38].
It's noteworthy that one study examining communication during cardiopulmonary resuscitation found a significant reduction in frustration scores, one of the six domains of the NASA-TLX, when standardized communication was used compared to closed-loop communication[39]. Alongside with other educational interventional study targeting stress-mitigation strategies[40] or team resources management techniques[41], these studies gave us insights into how to intervene with modifiable factors influencing workloads or stress.
The association between resuscitation performance and workload or stress experienced during resuscitation is intricate. However, any direct reporting of these two variables could potentially generate a misleading impression of their correlation. Given the limited number of studies specifically designed to manipulate workload and assess its impact on resuscitation performance, as well as that stress/workload may have different affect performance differently on individuals, the task force members have chosen not to include resuscitation performance in this PECOST. This decision was made to prevent conjecture and to maintain the integrity of the results.
Another issue of interest wound be NASA task load index. The original scale included six domains: 1)Mental Demand, 2)Physical Demand, 3)Temporal Demand, 4)Performance, 5)Effort 6)Frustration. Each of the six domains undoubtedly contributes to the overall workload, whereas the most prevalent method of measurement being the sum of sub-scores to derive a raw score as surrogate for workload. It is important to acknowledge that variations in the NASA-TLX raw score may predominantly stem from specific components of the score. For instance, the resuscitation team leader could potentially benefit from the use of cognitive aids and the appointment of designated nurse leaders, as these factors primarily impact mental workload.
Knowledge Gaps
- In this scoping review, we isolated factors influencing providers' workload, while excluding performance outcomes to maintain focus on our PECOST (Patient, Exposure, Comparison, Outcome, Study design, Time frame) question. Some of the included studies report not only interventions and measured workloads, but also resuscitation performances as well, such as chest-compression fractions, critical step compliances. This can be interpreted as association between intervention and performances but would not be suitable to imply the association between amount of workloads and resuscitation performances. Nonetheless, the association between workload/stress and resuscitation performance still holds considerable interest as this is the ultimate objective of translational science. More studies, with well-crafted experimental designs that consider the relationship between workloads and the performance of resuscitation teams, are needed to gain more insight into this complex issue of the interaction between factors, workloads, and performances.
- A limited number of studies have investigated healthcare providers' workload or stress during resuscitation on actual patients. This could potentially limit the applicability of our findings from the literature search. Certain factors may indeed influence providers' perceived workload, but the extent of this influence may vary in real clinical settings. More real-world studies that focus on provider workload and patient outcome are also required for further synthesis of knowledge.
- Task performance is closely associated with cognitive load, which refers to the capacity of an individual's mental resources to manage the total workload at any given moment. The studies reviewed employed the NASA Task Load Index and/or other stress markers as surrogates for workload, while overlooking individual differences in tolerance or capacity to handle workload. Influence of personal factors, contextual factors, and clinical experience in mitigating the impact of external stressors and perceived workload is also of interest as well.
Acknowledgement
The authors acknowledge the assistance provided by Mary-Doug Wright for developing the searching strategy. The following ILCOR EIT Taskforce Members are acknowledged as collaborators on this scoping review: Cristian Abelairas-Gomez, Jan Breckwoldt, Kathryn Eastwood, Nino Fijačko, Elaine Gilfoyle, Kasper G. Lauridsen, Jeffrey Lin, Andrew Lockey, Tasuku Matsuyama, Kevin Nation, Taylor L. Sawyer, Sebastian Schnaubelt, Ming-Ju Hsieh, Ying-Chih Ko, Joyce Yeung. We would like to thank Judith Finn and Peter Morley (ILCOR Science Advisory Committee) for their valuable contributions.
Attachments:
EIT 6401 Table 1
References
[1] Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. Adv Psychol 1988;52:139–83. https://doi.org/10.1016/s0166-4115(08)62386-9.
[2] LeBlanc VR. The Effects of Acute Stress on Performance: Implications for Health Professions Education. Acad Med 2009;84:S25–33. https://doi.org/10.1097/acm.0b013e3181b37b8f.
[3] Grös DF, Antony MM, Simms LJ, McCabe RE. Psychometric Properties of the State–Trait Inventory for Cognitive and Somatic Anxiety (STICSA): Comparison to the State–Trait Anxiety Inventory (STAI). Psychol Assessment 2007;19:369–81. https://doi.org/10.1037/1040-3590.19.4.369.
[4] Xia J, Nooraei N, Kalluri S, Edwards B. Spatial release of cognitive load measured in a dual-task paradigm in normal-hearing and hearing-impaired listeners. J Acoust Soc Am 2015;137:1888–98. https://doi.org/10.1121/1.4916599.
[5] Sweller J. Cognitive Load During Problem Solving: Effects on Learning. Cognitive Sci 1988;12:257–85. https://doi.org/10.1207/s15516709cog1202_4.
[6] Hunziker S, Johansson AC, Tschan F, Semmer NK, Rock L, Howell MD, et al. Teamwork and Leadership in Cardiopulmonary Resuscitation. J Am Coll Cardiol 2011;57:2381–8. https://doi.org/10.1016/j.jacc.2011.03.017.
[7] Monsieurs KG, Nolan JP, Bossaert LL, Greif R, Maconochie IK, Nikolaou NI, et al. European Resuscitation Council Guidelines for Resuscitation 2015 Section 1. Executive summary. Resuscitation 2015;95:1–80. https://doi.org/10.1016/j.resuscitation.2015.07.038.
[8] Roman A, Petersen T, McDermott K, Rajzer-Wakeham K, Rajapreyar P, Szadkowski A, et al. 599A: MEASURING WORKLOAD DURING ACTUAL PEDIATRIC RESUSCITATION EVENTS: A PILOT STUDY. Crit Care Med 2023;51:288–288. https://doi.org/10.1097/01.ccm.0000908124.98739.d5.
[9] Wagner M, Gröpel P, Eibensteiner F, Kessler L, Bibl K, Gross IT, et al. Visual attention during pediatric resuscitation with feedback devices: a randomized simulation study. Pediatr Res 2022;91:1762–8. https://doi.org/10.1038/s41390-021-01653-w.
[10] Grundgeiger T, Hahn F, Wurmb T, Meybohm P, Happel O. The use of a cognitive aid app supports guideline-conforming cardiopulmonary resuscitations: A randomized study in a high-fidelity simulation. Resusc Plus 2021;7:100152. https://doi.org/10.1016/j.resplu.2021.100152.
[11] Fernández-Ayuso D, Fernández-Ayuso R, Del-Campo-Cazallas C, Pérez-Olmo JL, Matías-Pompa B, Fernández-Carnero J, et al. The Modification of Vital Signs According to Nursing Students’ Experiences Undergoing Cardiopulmonary Resuscitation Training via High-Fidelity Simulation: Quasi-Experimental Study. Jmir Serious Games 2018;6:e11061. https://doi.org/10.2196/11061.
[12] Sellmann T, Oendorf A, Wetzchewald D, Schwager H, Thal SC, Marsch S. The Impact of Withdrawn vs. Agitated Relatives during Resuscitation on Team Workload: A Single-Center Randomised Simulation-Based Study. J Clin Medicine 2022;11:3163. https://doi.org/10.3390/jcm11113163.
[13] Butler L, Whitfill T, Wong AH, Gawel M, Crispino L, Auerbach M. The Impact of Telemedicine on Teamwork and Workload in Pediatric Resuscitation: A Simulation-Based, Randomized Controlled Study. Telemed E-Health 2019;25:205–12. https://doi.org/10.1089/tmj.2018.0017.
[14] Sellmann T, Alchab S, Wetzchewald D, Meyer J, Rassaf T, Thal SC, et al. Simulation-based randomized trial of medical emergency cognitive aids. Scand J Trauma Resusc Emerg Medicine 2022;30:45. https://doi.org/10.1186/s13049-022-01028-y.
[15] Willmes M, Sellmann T, Semmer N, Tschan F, Wetzchewald D, Schwager H, et al. Impact of family presence during cardiopulmonary resuscitation on team performance and perceived task load: a prospective randomised simulator-based trial. Bmj Open 2022;12:e056798. https://doi.org/10.1136/bmjopen-2021-056798.
[16] Badke CM, Friedman ML, Harris ZL, McCarthy-Kowols M, Tran S. Impact of an untrained CPR Coach in simulated pediatric cardiopulmonary arrest: A pilot study. Resusc Plus 2020;4:100035. https://doi.org/10.1016/j.resplu.2020.100035.
[17] Hunziker S, Pagani S, Fasler K, Tschan F, Semmer NK, Marsch S. Impact of a stress coping strategy on perceived stress levels and performance during a simulated cardiopulmonary resuscitation: a randomized controlled trial. Bmc Emerg Medicine 2013;13:8. https://doi.org/10.1186/1471-227x-13-8.
[18] Lacour M, Bloudeau L, Combescure C, Haddad K, Hugon F, Suppan L, et al. Impact of a Mobile App on Paramedics’ Perceived and Physiologic Stress Response During Simulated Prehospital Pediatric Cardiopulmonary Resuscitation: Study Nested Within a Multicenter Randomized Controlled Trial. Jmir Mhealth Uhealth 2021;9:e31748. https://doi.org/10.2196/31748.
[19] Brown LL, Lin Y, Tofil NM, Overly F, Duff JP, Bhanji F, et al. Impact of a CPR feedback device on healthcare provider workload during simulated cardiac arrest. Resuscitation 2018;130:111–7. https://doi.org/10.1016/j.resuscitation.2018.06.035.
[20] Müller MP, Hänsel M, Fichtner A, Hardt F, Weber S, Kirschbaum C, et al. Excellence in performance and stress reduction during two different full scale simulator training courses: A pilot study. Resuscitation 2009;80:919–24. https://doi.org/10.1016/j.resuscitation.2009.04.027.
[21] Roitsch CM, Hagan JL, Patricia KE, Jain S, Chen X, Arnold JL, et al. Effects of Team Size and a Decision Support Tool on Healthcare Providers’ Workloads in Simulated Neonatal Resuscitation: A Randomized Trial. Simul Healthc J Soc Simul Healthc 2020;16:254–60. https://doi.org/10.1097/sih.0000000000000475.
[22] Bjørshol CA, Myklebust H, Nilsen KL, Hoff T, Bjørkli C, Illguth E, et al. Effect of socioemotional stress on the quality of cardiopulmonary resuscitation during advanced life support in a randomized manikin study* Crit Care Med 2011;39:300–4. https://doi.org/10.1097/ccm.0b013e3181ffe100.
[23] Tofil NM, Cheng A, Lin Y, Davidson J, Hunt EA, Chatfield J, et al. Effect of a Cardiopulmonary Resuscitation Coach on Workload During Pediatric Cardiopulmonary Arrest: A Multicenter, Simulation-Based Study. Pediatr Crit Care Me 2020;21:e274–81. https://doi.org/10.1097/pcc.0000000000002275.
[24] Ontrup G, Vogel M, Wolf OT, Zahn PK, Kluge A, Hagemann V. Does simulation-based training in medical education need additional stressors? An experimental study. Ergonomics 2020;63:80–90. https://doi.org/10.1080/00140139.2019.1677948.
[25] Zehnder E, Law BHY, Schmölzer GM. Does parental presence affect workload during neonatal resuscitation? Archives Dis Child - Fetal Neonatal Ed 2020;105:559–61. https://doi.org/10.1136/archdischild-2020-318840.
[26] Corazza F, Snijders D, Arpone M, Stritoni V, Martinolli F, Daverio M, et al. Development and Usability of a Novel Interactive Tablet App (PediAppRREST) to Support the Management of Pediatric Cardiac Arrest: Pilot High-Fidelity Simulation-Based Study. Jmir Mhealth Uhealth 2020;8:e19070. https://doi.org/10.2196/19070.
[27] Gross IT, Whitfill T, Redmond B, Couturier K, Bhatnagar A, Joseph M, et al. Comparison of Two Telemedicine Delivery Modes for Neonatal Resuscitation Support: A Simulation-Based Randomized Trial. Neonatology 2020;117:159–66. https://doi.org/10.1159/000504853.
[28] Asselin N, Choi B, Pettit CC, Dannecker M, Machan JT, Merck DL, et al. Comparative Analysis of Emergency Medical Service Provider Workload During Simulated Out-of-Hospital Cardiac Arrest Resuscitation Using Standard Versus Experimental Protocols and Equipment. Simul Healthc J Soc Simul Healthc 2018;13:376–86. https://doi.org/10.1097/sih.0000000000000339.
[29] Pallas JD, Smiles JP, Zhang M. Cardiac Arrest Nurse Leadership (CANLEAD) trial: a simulation-based randomised controlled trial implementation of a new cardiac arrest role to facilitate cognitive offload for medical team leaders. Emerg Med J 2021;38:572–8. https://doi.org/10.1136/emermed-2019-209298.
[30] Finan E, Bismilla Z, Whyte HE, LeBlanc V, McNamara PJ. High-fidelity simulator technology may not be superior to traditional low-fidelity equipment for neonatal resuscitation training. J Perinatol 2012;32:287–92. https://doi.org/10.1038/jp.2011.96.
[31] Parsons SE, Carter EA, Waterhouse LJ, Sarcevic A, O’Connell KJ, Burd RS. Assessment of workload during pediatric trauma resuscitation. J Trauma Acute Care 2012;73:1267–72. https://doi.org/10.1097/ta.0b013e318265d15a.
[32] Lee K, Kim MJ, Park J, Park JM, Kim KH, Shin DW, et al. The effect of distraction by dual work on a CPR practitioner’s efficiency in chest compression. Medicine 2017;96:e8268. https://doi.org/10.1097/md.0000000000008268.
[33] Hunziker S, Pagani S, Fasler K, Tschan F, Semmer NK, Marsch S. Impact of a stress coping strategy on perceived stress levels and performance during a simulated cardiopulmonary resuscitation: a randomized controlled trial. Bmc Emerg Medicine 2013;13:8. https://doi.org/10.1186/1471-227x-13-8.
[34] Brown LL, Lin Y, Tofil NM, Overly F, Duff JP, Bhanji F, et al. Impact of a CPR feedback device on healthcare provider workload during simulated cardiac arrest. Resuscitation 2018;130:111–7. https://doi.org/10.1016/j.resuscitation.2018.06.035.
[35] Considine J, Eastwood K, Webster H, Smyth M, Nation K, Greif R, et al. Family presence during adult resuscitation from cardiac arrest: A systematic review. Resuscitation 2022;180:11–23. https://doi.org/10.1016/j.resuscitation.2022.08.021.
[36] Dainty KN, Atkins DL, Breckwoldt J, Maconochie I, Schexnayder SM, Skrifvars MB, et al. Family presence during resuscitation in paediatric and neonatal cardiac arrest: A systematic review. Resuscitation 2021;162:20–34. https://doi.org/10.1016/j.resuscitation.2021.01.017.
[37] JP W, MH W, MF de A, K A, W E-N, JG F, et al. Family Presence During Neonatal Resuscitation. International Liaison Committee on Resuscitation (ILCOR) Neonatal Life Support Task Force, Nov 2020, n.d. https://doi.org/http://ilcor.org.
[38] Lauridsen KG, Krogh K, Müller SD, Schmidt AS, Nadkarni VM, Berg RA, et al. Barriers and facilitators for in-hospital resuscitation: A prospective clinical study. Resuscitation 2021;164:70–8. https://doi.org/10.1016/j.resuscitation.2021.05.007.
[39] Lauridsen KG, Watanabe I, Løfgren B, Cheng A, Duval-Arnould J, Hunt EA, et al. Standardising communication to improve in-hospital cardiopulmonary resuscitation. Resuscitation 2020;147:73–80. https://doi.org/10.1016/j.resuscitation.2019.12.013.
[40] Sigwalt F, Petit G, Evain J-N, Claverie D, Bui M, Guinet-Lebreton A, et al. Stress Management Training Improves Overall Performance during Critical Simulated Situations. Anesthesiology 2020;133:198–211. https://doi.org/10.1097/aln.0000000000003287.
[41] Ghazali DA, Darmian-Rafei I, Ragot S, Oriot D. Performance Under Stress Conditions During Multidisciplinary Team Immersive Pediatric Simulations* Pediatr Crit Care Me 2018;19:e270–8. https://doi.org/10.1097/pcc.0000000000001473.