The health consequences of precarious employment experiences
Bibliographic record
Abstract
OBJECTIVE: This study provides a test of a conceptual framework of the stress-related health consequences of "precarious" employment experiences defined as those associated with instability, lack of protection, insecurity across various dimensions of work, and social and economic vulnerability. METHODS: Data were drawn from the Canadian Survey of Labor and Income Dynamics (SLID), a nationally representative longitudinal labor-market survey (1999-2004). Logistic regression analysis estimated the impact of several dimensions of precarious employment on two health outcomes: low health status and low functional health. PARTICIPANTS: For each calendar year we selected a subsample of individuals with close ties to the labor-market--i.e., aged 25 to 54, not full-time students, and employed at least 9 months of the year. We excluded individuals who were self-employed, those in management-level positions, and individuals who reported less than good health at the beginning of the year. RESULTS: Certain work characteristics (low earnings, the lack of an annual wage increase, substantial unpaid overtime hours, the absence of pension benefits, manual work) predict an increased risk of adverse general and/or functional health outcomes. CONCLUSIONS: Proactive regulatory initiatives and all-encompassing benefits programs are urgently required to address emerging work forms and arrangements that present risks to health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".