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Record W4410523326 · doi:10.1136/bmjoq-2025-qshu.56

56 Prioritizing healthcare professionals’ wellbeing: the development of a standardized debriefing tool following critical events

2025· article· en· W4410523326 on OpenAlexfundaboutno aff
Maria van Pelt, Theresa Morris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsDebriefingHealth professionalsHealth carePsychologyComputer scienceMedicineMedical educationPolitical science

Abstract

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Introduction Healthcare professionals have experienced high levels of emotional, physical, and mental hardships pre- and post-pandemic. Particularly, there have been high levels of moral distress, secondary traumatic stress, compassion fatigue, and burnout within groups who experience a high volume of critical events. 1 2 Critical events are those that can induce stress or hardship to a healthcare professional, including, but not limited to respiratory or cardiac resuscitations, unforeseen patient deaths, medical futility, medical errors, and patient/visitor violence. High levels of critical events can lead to decreased job satisfaction and intention to leave healthcare, as well as decreased wellbeing and mental health concerns.3 This not only affects individual professionals but has a ripple effect on the healthcare system and patients. Healthcare professional burnout is widespread and has become one of the top priorities of the United States Surgeon General’s office.4 It is imperative that the mental health and wellbeing of healthcare professionals be prioritized, and interventions implemented.One intervention that has shown benefits in moral distress, secondary traumatic stress, compassion fatigue, and burnout of healthcare professionals is debriefing.3–6 Debriefing after critical events can be done immediately after an event, or days to weeks after an event.6 Implementation of a post-critical event debriefing process has shown increases in compassion and work satisfaction levels of staff.6 According to multiple studies, moral distress levels decreased following regular debriefing sessions within a variety of hospital units.5 7 By allowing staff to participate in a comprehensive debriefing, it can provide an opportunity to process emotions and potentially overcome moral distress and other wellbeing threats.7 Additionally, there were improvements in staff sick time used and decreased staff vacancies.8 Therefore, this study aimed to develop an evidenced-based standardized debriefing tool for use after critical events to promote healthcare professional mental health and wellbeing. This study received institutional review board approval as exempt.Methods An evidenced-based standardized debriefing tool was developed by integrating two established frameworks to guide post-critical incident discussions in healthcare settings. The first framework is the U.S. Surgeon General’s framework for workplace mental health and wellbeing. It comprises five essential categories necessary for optimal employee wellbeing and mental health. 9 The categories include protection from harm, connection and community, work-life harmony, mattering at work, and opportunity for growth.9 The second framework utilized in the debriefing tool is derived from a five-phase educational debriefing model for medical simulations. These phases include an introduction to debriefing, a defusing phase, a discovering phase, a deepening phase, and a closing phase of debriefing.10 Using a modified Delphi design, an interdisciplinary expert panel of seven healthcare professionals with expertise in critical events, peer support, debriefing, or healthcare professional burnout in the healthcare setting was convened. During the first survey round, the panel evaluated the quality and rigor of the debriefing tool. During the second round, the tool was evaluated for content, clarity, and functionality according to the Mini-Checklist (MiChe), a validated instrument with high interrater reliability (ICC = 0.755; P < 0.001) in appraising methodological guideline quality.11 Consensus was defined as 80% or greater agreement among raters.Results Greater than 80% consensus was achieved in round one quantitative questions with a Gwets-AC2 of 0.93 for interrater reliability. Thematic analysis using the Braun and Clarke methodology was performed for qualitative questions which guided revisions to the debriefing tool. Round two resulted in 100% consensus for all questions.The use of this evidence-based debriefing tool could significantly reduce provider burnout by creating structured opportunities for emotional processing and peer support following critical events. This focus on professional wellbeing through structured debriefing could lead to improved job satisfaction, reduced turnover, and ultimately, more resilient healthcare teams. The development of this evidence-based debriefing tool has highlighted several potential implementation considerations. We anticipate that success will depend on early stakeholder engagement, dedicated training time for staff, and clear integration into existing workflows. Strong leadership support and champions within each department would be crucial for driving adoption. Regular feedback mechanisms and flexibility to adapt the tool based on user experience would be essential for sustained implementation. Lastly, consideration of resource constraints, particularly time pressures in clinical settings, would need to be carefully addressed in the implementation strategy. These anticipated challenges and success factors could inform the future implementation plan for the debriefing tool.References Epstein EG, Haizlip J, Liaschenko J, Zhao D, Bennett R, Faith M. Moral Distress, Mattering, and Secondary Traumatic Stress in Provider Burnout: A Call for Moral Community. AACN ADV CRIT CARE 2020;31(2):146–157. doi:10.4037/aacnacc2020285 Harvey G, Tapp DM. Exploring the meaning of critical incident stress experienced by intensive care unit nurses. Nursing Inquiry 2020;27(4):e12365. doi:10.1111/nin.12365 Arbios D, Srivastava J, Gray E, Murray P, Ward J. Cumulative stress debriefings to combat compassion fatigue in a pediatric intensive care unit. AM J CRIT CARE 2022;31(2):111–118. doi:10.4037/ajcc2022560 Health Worker Burnout. U.S. department of health and human services. Office of the Surgeon General. Updated on August 2, 2024. Accessed on October 28, 2024. https://www.hhs.gov/surgeongeneral/priorities/health-worker-burnout/index.htmlBrowning ED, Cruz JS. Reflective debriefing: a social work intervention addressing moral distress among ICU nurses. Journal of Social Work in End-of-Life & Palliative Care 2018;14(1):44–72. doi:10.1080/15524256.2018.1437588 Nerovich C, Derrington SF, Sorce LR, Manzardo J, Manworren RCB. Debriefing after critical events is feasible and associated with increased compassion satisfaction in the pediatric intensive care unit. Crit Care Nurse 2023;43(3):19–27. doi:10.4037/ccn2023842 Shashidhara S, Kirk S. Moral distress: a framework for offering relief through debrief. doi:10.1086/JCE2020314364 Folz E. Implementation of a critical incidence stress management program at a tertiary care hospital. CAN J CRIT CARE NURS. 2018;29(2):37–38.Workplace Mental Health & Well-Being. U.S. Department of Health and Human Services. Office of the Surgeon General. Updated on May 30, 2024. https://www.hhs.gov/surgeongeneral/priorities/workplace-well-being/index.htmlZigmont JJ, Kappus LJ, Sudikoff SN. The 3D Model of Debriefing: Defusing, Discovering, and Deepening. Seminars in Perinatology 2011;35(2):52–58. doi:10.1053/j.semperi.2011.01.003 Siebenhofer A, Semlitsch T, Herborn T, Siering U, Kopp I, Hartig J. Validation and reliability of a guideline appraisal mini-checklist for daily practice use. BMC Med Res Methodol. 2016;16(1):39. doi:10.1186/s12874-016-0139-x

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.062
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.062
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.102
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0030.004
Open science0.0020.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0140.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.463
Teacher spread0.415 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
Admission routes2
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