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Record W2021227002 · doi:10.1002/smi.1012

Burnout, stress and health of employees on non‐standard work schedules: a study of Canadian workers

2004· article· en· W2021227002 on OpenAlexaffabout
Muhammed Jamal

Bibliographic record

VenueStress and Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsConcordia University
FundersNature
KeywordsBurnoutEmotional exhaustionShift workPsychologyJob satisfactionWork (physics)Metropolitan areaOccupational burnoutWork shiftJob stressWork stressApplied psychologySocial psychologyClinical psychologyMedicinePsychiatryOperations management

Abstract

fetched live from OpenAlex

Abstract This study examined the relationship between non‐standard work schedules (shift work and weekend work) and job burnout, stress and psychosomatic health problems among full‐time employed Canadians in a large metropolitan city on the east coast. Data were collected by means of a structured mail back questionnaire (N = 376). Employees involved with weekend work reported significantly higher emotional exhaustion, job stress and psychosomatic health problems than employees not involved with weekend work. Similarly, employees on non‐standard work shifts (other than fixed day shift, 9 a.m.–5 p.m.) reported significantly higher overall burnout, emotional exhaustion, job stress and health problems than employees on a fixed day shift. Results from two‐way ANOVA indicated that employees involved with weekend work and non‐fixed day shifts reported significantly higher emotional exhaustion and health problems than other employees. Implications of the findings are discussed for future researchers in light of employee well‐being and non‐standard work schedules. Copyright © 2004 John Wiley & Sons, Ltd.

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)0.001
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 score0.025
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.046
GPT teacher head0.392
Teacher spread0.346 · 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

Citations140
Published2004
Admission routes2
Has abstractyes

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