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Record W1990343414 · doi:10.1097/mej.0000000000000194

Quality of work life, burnout, and stress in emergency department physicians

2014· review· en· W1990343414 on OpenAlexaff
Isabelle Bragard, Gilles Dupuis, Richard Fleet

Bibliographic record

VenueEuropean Journal of Emergency Medicine · 2014
Typereview
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsCégep de LévisUniversité LavalCentre de Liaison Sur l'Intervention et la Prévention PsychosocialesUniversité du Québec à Montréal
Fundersnot available
KeywordsBurnoutPsycINFOEmergency departmentJob satisfactionMEDLINEMedicineOccupational stressFamily medicinePsychologyClinical psychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

A 2006 literature review reported that emergency department (ED) physicians showed elevated burnout levels and highlighted several environment and personal issues contributing toward burnout. Research on burnout in EDs is limited. We propose an updated qualitative review on the relationships between work stress, burnout, and quality of work life in ED physicians. We searched MEDLINE, PsycInfo, and Science Direct for studies published since 2005. Of 491 papers, 10 papers were retained, using validated measures and having a minimum of 75 participants. Data extraction was performed manually by the first author and was reviewed by the second author. The majority of the studies used large samples, cross-sectional designs, random, and/or stratified assignment. ED physicians showed moderate to high levels of burnout with difficult work conditions including significant psychological demands, lack of resources, and poor support. Nonetheless, physicians reported high job satisfaction. Further studies should focus on the implementation of measures designed to prevent burnout.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.007
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.199
GPT teacher head0.505
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations187
Published2014
Admission routes1
Has abstractyes

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