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Record W3207419187 · doi:10.1097/jom.0000000000002396

Longitudinal Reciprocal Relationships Between the Psychosocial Work Environment and Burnout

2021· article· en· W3207419187 on OpenAlexaffabout

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsToronto Public HealthInstitute for Work & Health
Fundersnot available
KeywordsBurnoutReciprocalPsychosocialOccupational burnoutWork environmentOccupational stressJob strainWork (physics)

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine longitudinal reciprocal relationships between the psychosocial work environment and burnout. METHODS: We used two-wave cross-lagged panel models to estimate associations between a wide range of psychosocial work factors (ie, job demands, job control, job insecurity, coworker support, supervisor support, and organizational justice) and burnout in a broadly representative sample of the general working population in Canada (n = 453). RESULTS: Bidirectional associations between the psychosocial work environment and burnout were observed. Results supported the causal predominance of psychosocial work factors over burnout. Higher job demands, lower job control, higher job insecurity, and lower organizational justice predicted burnout over time. Burnout only predicted lower supervisor support over time. CONCLUSIONS: Our findings suggest that stress at work is better understood as a cause rather than a consequence of burnout in the general working population.

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)0.005
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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.123
GPT teacher head0.411
Teacher spread0.288 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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