Occupational Gradients in Work-Related Insecurity and Health: Interrogating the Links
Bibliographic record
Abstract
Traditional work-related securities that constitute the career-job model of employment have been in steep decline for several decades, affecting workers across industries and occupations. Still, insecure employment remains unequally distributed across the working population according to the major axes of social stratification, namely age, gender, race, and socioeconomic class. This study investigates patterns of exposure to work-related insecurity across the occupational hierarchy and whether these contribute to occupational gradients in health outcomes. Drawing on data from a national panel survey of the Canadian workforce, a multilevel growth curve modeling approach is used to examine the relationship between work-insecurity exposures and workers’ self-rated health trajectories over 5 years. Findings show that work-related insecurity is associated with declines in self-rated health, although the type of insecurity as well as the magnitude, direction, and duration of the effect varies by occupational status-position. The application of pseudo-R 2 tests confirmed this study’s central hypothesis that gradients in health outcomes across occupational hierarchies are due, in part, to differences in exposure to work-related insecurity. Going forward, the development of effective health promotion interventions that can modify work-related health gradients, must work toward mitigating the risk of exposure to adverse work circumstances that is systemic to occupational hierarchies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".