Analysis of the 2012 Canadian Community Health Survey-Mental Health demonstrates employment insecurity to be associated with mental illness
Bibliographic record
Abstract
ABSTRACT: A growing number of people depend on flexible employment, characterized by outsider employment and lower levels of job security. This study investigated whether there was a synergistic effect of employment status and job insecurity on mental disorders.This study used data from the 2012 Canadian Community Health Survey-Mental Health (CCHS) of 13,722 Canada's labor force population aged 20 to 70. Data were collected from January to December, 2012, using computer-assisted personal interviewing. As combining employment status with perceived job insecurity, we formed five job categories: secure full-time, full-time insecure, part-time secure, part-time insecure employment, and unemployment.Results showed that, regardless of employment status (full-time vs part-time), insecure employment was significantly associated with high risk of mental disorders. Furthermore, the odds ratios for insecure employment were similar to those for unemployment. Male workers who are full-time, but with insecure jobs, were more likely to experience mental disorders than female workers.This study's findings imply that while perceived job insecurity may be a critical factor for developing mental health problems among workers, providing effective health care services can mitigate an excessive health risk for the most vulnerable employment, especially for insecure part-time employment and unemployment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".