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Association of Sleep Disorders With Physician Burnout

2020· article· en· W3097228656 on OpenAlexaff
Matthew D. Weaver, Rebecca Robbins, Stuart F. Quan, Conor S. O’Brien, Natalie Viyaran, Charles A. Czeisler, Laura K. Barger

Bibliographic record

VenueJAMA Network Open · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsCanadian Sleep & Circadian Network
FundersNational Institute for Occupational Safety and HealthNational Heart, Lung, and Blood InstituteBrigham Research InstituteNational Institutes of HealthBrigham and Women's Hospital
KeywordsBurnoutSleep (system call)Association (psychology)MedicineCross-sectional studyPsychiatryFamily medicineClinical psychologyPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

Physicians’ mental health concerns affect the quality of life of caregivers, patient safety, health care expenditures, and occupational turnover. More than half of US physicians report burnout. Sleep deficiency is common—often a consequence of rotating or extended-duration shifts, night call, and competing demands. Sleep disturbance is a predictor of depression, and insufficient sleep may contribute to the development of burnout. Medical residents report that prolonged work hours negatively affect their quality of life. These factors suggest that sleep deficiency may be an underlying contributor to poor mental health in physicians. We sought to identify the prevalence of sleep disorders and estimate the cross-sectional association between sleep disorders and burnout symptoms among faculty and staff in a large teaching hospital system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.376
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2020
Admission routes1
Has abstractyes

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