A Novel Measure of Work Stress: Identifying Work Stressor Patterns in Canada Using Latent Class Analysis
Bibliographic record
Abstract
This analysis utilizes data from the 2012 Mental Health component of the Canadian Community Health Survey (CCHS-MH) and latent class analysis to identify patterns of stressful work environments and their relationship with occupational and social location. Based on the intersection of 12 work stress measures, five classes of stressful work environments emerged that can be described as low stress, high stress, physical stress, monotonous, and chaotic environments. Results from models including covariates show that work stress exposure is stratified by occupation, socioeconomic status, age, gender, race/ethnicity, immigrant status, and marital status. Notably, blue- and pink-collar workers had higher odds of experiencing patterns of high stress and physical stress. With some exceptions, less educated, lower income workers, as well as women and younger workers, were more likely to experience all patterns of stressful work environments compared to experiencing low stress.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".