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Record W4394767624 · doi:10.29158/jaapl.230078-23

Contributors to Physician Burnout and Well-Being in Forensic Psychiatrists in Canada.

2024· article· en· W4394767624 on OpenAlexaffabout
Treena Wilkie, Roland M. Jones, Lisa Ramshaw, Graham Glancy, Lindsay Groat, Sumeeta Chatterjee

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsBurnoutCompassion fatiguePsychological interventionPsychologyMental healthForensic psychiatryNursingMedicinePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

The experience of burnout in forensic psychiatrists has not been well studied, with most studies focusing on the experiences of forensic nurses, the impact of vicarious trauma and compassion fatigue in forensic mental health professionals, and the risk of posttraumatic stress disorder related to workplace exposures. This study reports on a national survey (34% response rate) conducted with forensic psychiatrists across Canada to understand the rate of, and contributors to, burnout and professional fulfillment. Just over half of the physician respondents reported experiencing burnout, which is in line with other recent surveys in Canada that have indicated elevated levels of burnout since the onset of the pandemic. The highest rates were found among early-career psychiatrists and those whose values did not align with their workplace. Intellectual stimulation, the interface with the legal system, and flexibility in one's job were all strongly linked with professional fulfillment. The goal of this survey was not only to identify rates and variables affecting burnout and wellness in this population but also to expand the dialogue on potential interventions at institutional and systems levels that can reduce burnout, promote professional fulfillment, and enhance recruitment and retention in the field of forensic psychiatry.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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 designObservational
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

Citations2
Published2024
Admission routes2
Has abstractyes

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