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Record W2042716155 · doi:10.1097/jom.0b013e3181ed3d80

Workplace Characteristics, Depression, and Health-Related Presenteeism in a General Population Sample

2010· article· en· W2042716155 on OpenAlexafffundabout
JianLi Wang, Norbert Schmitz, Elizabeth Smailes, Jitender Sareen, Scott B. Patten

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsPresenteeismPsychosocialJob strainDepression (economics)Affect (linguistics)PopulationJob performancePsychologyMedicineOccupational safety and healthJob satisfactionGerontologyAbsenteeismEnvironmental healthPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the relationships between workplace psychosocial factors, work/family conflicts, depression, and health-related presenteeism in a sample of employees who were randomly selected from the communities. METHODS: A cross-sectional study of 4032 employees representative of the working population aged 25 to 64 years in Alberta, Canada. Data about workplace characteristics, depression, and health-related presenteeism were collected through telephone. RESULTS: In the participants, 47.3% and 42.9% reported some degree of impaired job performance in completing work and avoiding distraction, respectively. Major depression is the strongest factor associated with avoiding distraction. Job strain and effort-reward imbalance seemed to affect job performance through severity of depression but not major depression. CONCLUSIONS: Negative work environment may directly and indirectly affect job performance. Workplace health promotion activities should target organizational factors such as job strain and effort-reward imbalance and work/family conflicts so as to reduce the risk of depression and the direct and indirect effects of these risk factors and depression on productivity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.365
Teacher spread0.338 · 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.

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

Citations85
Published2010
Admission routes3
Has abstractyes

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