A Brief Tool to Screen Patients for Precarious Employment: A Validation Study
Bibliographic record
Abstract
PURPOSE: Precarious employment, defined by temporary contracts, unstable employment, or job insecurity, is increasingly common and is associated with inconsistent access to benefits, lower income, and greater exposure to physical and psycholosocial hazards. Clinicians can benefit from a simple approach to screen for precarious employment to improve their understanding of a patient's social context, help with diagnoses, and inform treatment plans and intersectional interventions. Our objective was to validate a screening tool for precarious employment. METHODS: We used a 3-item screening tool that covered key aspects of precarious employment: non-standard employment, variable income, and violations of occupational health and safety rights and protections. Answers were compared with classification using the Poverty and Employment Precarity in Southern Ontario Employment Index. Participants were aged 18 years and older, fluent in English, and employed. They were recruited in 7 primary care clinic waiting rooms in Toronto, Canada over 12 months. RESULTS: A total of 204 people aged 18-72 years (mean 38 [SD 11.3]) participated, of which 93 (45.6%) identified as men and 119 (58.3%) self-reported as White. Participants who reported 2 or more of the 3 items as positive were almost 4 times more likely to be precariously employed (positive likelihood ratio = 3.84 [95% CI, 2.15-6.80]). CONCLUSIONS: A 3-item screening tool can help identify precarious employment. Our tool is useful for starting a conversation about employment precarity and work conditions in clinical settings. Implementation of this screening tool in health settings could enable better targeting of resources for managing care and connecting patients to legal and employment support services.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".