Workplace Characteristics, Depression, and Health-Related Presenteeism in a General Population Sample
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".