Collecting occupation and hazards information in primary care using O*NET
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the feasibility of collecting occupation and occupational hazard data in a primary care setting, using the Occupational Information Network (O*NET) database to assist with classification. METHODS: We collected data from 204 employed adult primary care patients in Toronto, Canada, on their occupation and exposure to occupational hazards, and mapped their job titles to the O*NET database. We compared their self-reported occupational hazard exposures with the likelihood of exposure on O*NET. RESULTS: Exposure to repetitive arm movement was reported by 78%, to vapors/gas/dust/fumes by 30%, to noise by 30%, and to heavy loads by 31%. Significant differences in exposure to vapors/gas/dust/fumes were associated with work precarity. We matched the majority of job titles (89%) to O*NET categories. CONCLUSIONS: Collecting employment information in primary care setting was feasible, with the majority of job titles mapping onto O*NET classifications.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".