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Record W2291562260 · doi:10.1093/pch/16.7.409

Burnout among faculty physicians in an academic health science centre

2011· article· en· W2291562260 on OpenAlexaff
James G. Wright, Nicole Khetani, Derek Stephens

Bibliographic record

VenuePaediatrics & Child Health · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsBurnoutHealth scienceMedical educationMedicinePsychologyFamily medicineNursingClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Burnout experienced by physicians is concerning because it may affect quality of care. OBJECTIVE: To determine the frequency of burnout among physicians at an academic health science centre and to test the hypothesis that work hours are related to burnout. METHODS: All 300 staff physicians, contacted through their personal e-mail, were provided an encrypted link to an anonymous questionnaire. The primary outcome measure, the Copenhagen Burnout Inventory, has three subscales: personal, work related and patient related. RESULTS: The response rate for the questionnaire was 70%. Quantitative demands, insecurity at work and job satisfaction affected all three components of burnout. Of 210 staff physicians, 22% (n=46) had scores indicating personal burnout, 14% (n=30) had scores indicating work-related burnout and 8% (n=16) had scores indicating patient-related burnout. The correlation between total hours worked and total burnout was only 0.10 (P=0.14) DISCUSSION: Up to 22% of academic paediatric physicians had scores consistent with mild to severe burnout. A simple reduction in work hours is unlikely to be successful in reducing burnout and, therefore, quantitative demands, job satisfaction and work insecurity may require attention to address burnout among academic physicians.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.005
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 head0.087
GPT teacher head0.428
Teacher spread0.341 · 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 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

Citations52
Published2011
Admission routes1
Has abstractyes

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