Work and Mental Health - An Analysis of Canadian Community Health Survey
Bibliographic record
Abstract
Workplace mental health is a major concern in Canada. The primary objective of this research is to describe the relationship between work and mental health, paying particular attention to work stressors and further explore moderators and mediators of any relationships, that might be targeted in future intervention strategies. The source of the data is the two cycles of the Canadian Community Health Survey conducted by Statistics Canada. All estimates produced from the data were weighted to represent the target Ontario population using the weights provided by Statistics Canada. Estimates of the prevalence of the mental disorders and substance dependence and mean scores of work stressors, according to different groups of workers, were calculated. To examine health care use, we treated consultation with a mental health professional, use of medication-antidepressants and utilization of any resource as dependent variables. Bivariate relationships between mental health disorders and other variables explored the correlates of mental health disorders. Logistic regression was used to examine moderators and mediators of work stressors in relation to mental health disorders by including some socio-demographic variables and behavioral variables as covariates and we also included terms of their interactions with work stressors. Since the level of work stressors varied by occupation and was likely determined in part by occupation, we did not include both variables in regression analyses. Further regressions with health care utilization as the dependent variable were conducted with work stressors and occupation as independent variables and other variables as covariates and in interaction terms for people with mental health disorders. The results of the study suggest that there is strong association between work and mental health problems. The findings regarding work stressors and occupation as predictors of mental health problems suggest that work health and safety practitioners must continue to pay attention to the psychosocial conditions of work. We also explored what factors predicted whether people consulted a mental health professional (CCHSl.l) or whether they used any resource available to deal with their problem (CCHS1.2).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".