1804 – Mental Health At Work, Structures Of Daily Life And Individual Characteristics
Bibliographic record
Abstract
Aims To analyse variations in workers psychological distress, depression and burnout within a model encompassing the stress promoted by constraints-resources embedded in structures of daily life (workplace, family, social networks outside the workplace) and worker individual characteristics (demography, physical health, psychological traits, life habits, stressful childhood events). Methods Data were collected in 2009-2012 from a sample of 64 Quebec (Canada)workplaces, where 2162 employees were surved, for a response rate of 73.1%. Multilevel regression models were used to analyse the data. Results Variables explain 31.8% of psychological distress (GHQ), 47.7% of depression (BDI) and 48.1% of burnout (MBI). Associations are not the same for each outcome. Skill utilization (BDI, MBI), decision authority (GHQ), abusive supervision (BDI, MBI), conflicts (BDI, MBI) and job security (GHQ, BDI, MBI) are related to the outcomes. For the family, being in couple (BDI, MBI), minor children (BDI, MBI), family to work conflict (MBI), work-to-family conflict (GHQ, BDI, MBI), strained marital and parental relations (GHQ, BDI) associated with the outcomes. Social support outside the workplace predicts both psychological distress and depression. Most of the individual characteristics correlated with the three outcomes. Conclusions The results of this study suggest expanding approaches in occupational mental health in order to avoid coming to erroneous conclusions about the relationship between work and mental health. Depression and burnout seem to share a similar explanatory structure, while psychogical distress appear mostly explained by non-work factors.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".