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Record W3005843648 · doi:10.1007/s40037-020-00567-3

Bridging the gap: Responding to resident burnout and restoring well-being

2020· article· en· W3005843648 on OpenAlexaff
Ana Hategan, Tara Riddell

Bibliographic record

VenuePerspectives on Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBurnoutAttendanceMedicineCurriculumMedical educationPsychological resilienceFamily medicinePsychologyNursingClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

There is an increasing awareness of high burnout found among physicians. Resident physicians particularly face heightened stress due to inherent pressures of training in addition to systemic challenges common to healthcare. It is crucial that medical training programs and organizations create a culture which promotes physician well-being. We conducted an evaluation of a quality assurance pilot program aimed at creating a safe space for increasing burnout awareness and well-being among resident physicians. The program was voluntary, offered to psychiatry residents enrolled at McMaster University, and comprised an online resilience curriculum, peer groups, and wellness newsletters. Data analysis took place between December 15, 2018 and July 15, 2019. The educational goals were evaluated by outcome measures obtained over time in aggregated response data through residents' anonymous survey feedback. All aspects of the triad received positive feedback, with peer groups being perceived as most helpful. Of all residents, 31% (n = 22) engaged in all three aspects of the program; the majority were female (83%) and senior residents (63%). While 48% reported burnout upon enrollment, there was an average 50% stress reduction perceived post-attendance. This project has shown that peer groups can make a difference in the daily experience of psychiatry residents at our institution.

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)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.047
GPT teacher head0.449
Teacher spread0.401 · 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.

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".

Quick stats

Citations23
Published2020
Admission routes1
Has abstractyes

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