MétaCan
Menu
Retour à la cohorte
Enregistrement 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 sur OpenAlexfundaboutno aff
Maria van Pelt, Theresa Morris

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineHealth Professions
ThématiqueDisaster Response and Management
Établissements canadiensnon disponible
Organismes subventionnairesCanadian Institutes of Health ResearchAlberta Innovates
Mots-clésDebriefingHealth professionalsHealth carePsychologyComputer scienceMedicineMedical educationPolitical science

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,062
score de la tête « metaresearch » (Gemma)0,102
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,062
Score d'incertitude au seuil0,327

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0620,102
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,003
Communication savante0,0030,004
Science ouverte0,0020,007
Intégrité de la recherche0,0020,006
Charge utile insuffisante (le modèle a refusé de juger)0,0140,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,048
Tête enseignante GPT0,463
Écart entre enseignants0,415 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission2
Résumé présentoui

Explorer davantage

Même sujetDisaster Response and ManagementTravaux en français237 207