Psychosocial and other working conditions: Variation by employment arrangement in a sample of working Australians
Bibliographic record
Abstract
BACKGROUND: The evidence linking precarious employment with poor health is mixed. Self-reported occupational exposures in a population-based Australian sample were assessed to investigate the potential for differential exposure to psychosocial and other occupational hazards to contribute to such a relationship, hypothesizing that exposures are worse under more precarious employment arrangements (EA). METHODS: Various psychoscial and other working conditions were modeled in relation to eight empirically derived EA categories with Permanent Full-Time (PFT) as the reference category (N = 925), controlling for sex, age, and occupational skill level. RESULTS: More precarious EA were associated with higher odds of adverse exposures. Casual Full-Time workers had the worst exposure profile, showing the lowest job control, as well as the highest odds of multiple job holding, shift work, and exposure to four or more additional occupational hazards. Fixed-Term Contract workers stood out as the most likely to report job insecurity. Self-employed workers showed the highest job control, but also the highest odds of long working hours. CONCLUSIONS: Psychosocial and other working conditions were generally worse under more precarious EA, but patterns of adverse occupational exposures differ between groups of precariously employed workers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".