Is Job Insecurity Worse for Mental Health Than Having a Part-time Job in Canada?
Bibliographic record
Abstract
OBJECTIVES: A growing number of people depend on flexible employment, characterized by outsider employment status and perceived job insecurity. This study investigated whether there was a synergistic effect of employment status (full-time vs. part-time) and perceived job insecurity on major depressive disorder. METHODS: Data were derived from the 2012 Canadian Community Health Survey-Mental Health of 12 640 of Canada's labor force population, aged 20 to 74. By combining employment status with perceived job insecurity, we formed four employment categories: full-time secure, full-time insecure, part-time secure, and part-time insecure. RESULTS: Results showed no synergistic health effect between employment status and perceived job insecurity. Regardless of employment status (full-time vs. part-time), insecure employment was significantly associated with a high risk of major depressive disorder. Analysis of the interaction between gender and four flexible employment status showed a gender-contingent effect on this link in only full-time insecure category. Men workers with full-time insecure jobs were more likely to experience major depressive disorders than their women counterparts. CONCLUSIONS: This study's findings imply that perceived job insecurity may be a critical factor for developing major depressive disorder, in both men and women workers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".