The role of nonstandard and precarious jobs in the well‐being of disabled workers during workforce reintegration
Bibliographic record
Abstract
BACKGROUND: Nonstandard employment arrangements are becoming increasingly common and could provide needed flexibility for workers living with disabilities. However, these arrangements may indicate precarious employment, that is, employment characterized by instability, powerlessness, and limited worker rights and benefits. Little is known about the role of nonstandard and precarious jobs in the well-being of disabled persons during workforce reintegration after permanent impairment from work-related injuries or illnesses. METHODS: We used linked survey and administrative data for a sample of 442 Washington State workers who recently returned to work and received a workers' compensation permanent partial disability award after permanent impairment from a work-related injury. Multivariable logistic regression models were used to examine associations between nonstandard employment and outcomes related to worker well-being and sustained employment. We also examined associations between a multidimensional measure of precarious employment and these outcomes. Secondarily, qualitative content analysis methods were used to code worker suggestions on how workplaces could support sustained return to work (RTW). RESULTS: Workers in: (1) nonstandard jobs (compared with full-time, permanent jobs), and (2) precarious jobs (compared with less precarious jobs) had higher adjusted odds of low expectations for sustained RTW. Additionally, workers in precarious jobs had higher odds of reporting fair or poor health and unmet need for disability accommodation. Workers in nonstandard and precarious jobs frequently reported wanting safer and adequately staffed workplaces to ensure safety and maintain sustained employment. CONCLUSIONS: Ensuring safe, secure employment for disabled workers could play an important role in their well-being and sustained RTW.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 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".