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Record W4416947109 · doi:10.2196/78575

Facilitated Peer Discussion for Promoting Better Resident Wellness in Anesthesia Trainees: Qualitative Program Evaluation

2025· article· en· W4416947109 on OpenAlexaffvenue
Miku Wake, Nicholas West, Jessica Luo, Nancy E. Wang, J. Taylor, Kyra Moura, Theresa Newlove, Zoë Brown

Bibliographic record

VenueJMIR Perioperative Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsProvincial Health Services AuthorityBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsQualitative researchPeer reviewMEDLINEProgram evaluationPeer groupPeer support

Abstract

fetched live from OpenAlex

Background: Anesthesia residents experience nonroutine clinical events during perioperative patient care, including workplace stressors or adverse incidents that may cause physical and emotional stress. These events can lead to burnout and negative mental health outcomes. Burnout and depression rates are lower when residents have adequate support systems within their workplace. Better resident wellness (BREW) Rounds are a weekly 1-hour peer discussion for anesthesia residents, facilitated by a registered psychologist at our institution. Although shown to improve residents' well-being, a deeper understanding of the benefits of such programs may support their expansion to other residency programs. Objective: This study aimed to explore the benefits and most effective features of BREW Rounds to guide the development of similar programs at other institutions. Methods: Following research ethics board approval, we conducted a qualitative descriptive study based on semistructured interviews with anesthesia residents who had participated in one or more BREW sessions and with the main BREW Rounds facilitator. Topics of discussion included community building, belonging, mentorship, facilitation, discussion of nonclinical aspects, and removal of hierarchy. Interviews were conducted on videoconferencing software by researchers who were not involved in supervising or assessing the trainees. Audio recordings were auto-transcribed, deidentified, verified, and interpreted using thematic content analysis. Further perspectives on BREW Rounds were obtained from staff anesthesiologists through an anonymous online survey. Results: We interviewed 10 residents (6 junior, 3 senior, and 1 transition-to-practice) and 1 facilitator. Emerging themes included (1) access to a safe space free of judgment, allowing participants to be vulnerable about clinical or nonclinical aspects of their training, (2) building a sense of community in a fast-paced and often isolating environment, (3) providing opportunities for mentorship between junior and senior residents in a frequently changing colleague network, (4) the characteristics that create a "BREW culture", such as behavior norms during sessions and staff respect for protected time, (5) the importance of a good facilitator from outside the anesthesia department, especially during smaller sessions, (6) expanding BREW Rounds to other institutions, and (7) areas for improvement for the current program. Sixteen anesthesiology staff survey responses were available for analysis: 12/16 (75%) anesthesiologists supported residents leaving their clinical duties early for BREW Rounds and 12/16 (75%) believed BREW Rounds benefitted residents' well-being. Conclusions: This qualitative study confirms previous findings that BREW Rounds are beneficial to anesthesia training, improve the psychological wellness of residents, and may positively contribute to patient care. Program directors should recognize their potential positive impact on the learning environment, ensure that all staff and trainees understand the need to create protected time for this activity, consider partnering with wellness initiatives at the institutions in which residents are training, and endeavor to identify experienced and unbiased facilitators to moderate sessions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.125
GPT teacher head0.560
Teacher spread0.435 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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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Citations0
Published2025
Admission routes2
Has abstractyes

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