Examining Associations Between Job Characteristics and Health: Linking Data From the Occupational Information Network (O*NET) to Two U.S. National Health Surveys
Bibliographic record
Abstract
OBJECTIVE: To determine whether the Occupational Information Network (O*NET) database can be used to identify job dimensions to serve as proxy measures for psychosocial factors and select environmental factors, and to determine whether these factors could be linked to national health surveys to examine associations with health risk behaviors and outcomes. METHODS: Job characteristics were obtained from O*NET 98. Health outcomes were obtained from two national surveys. Data were linked using Bureau of Census codes. Multiple logistic regression was used to examine associations between O*NET factors and cardiovascular disease, depression, and health risk factors. RESULTS: Seven of nine work organization or psychosocial factors were significantly associated with health risk behaviors in both the National Health and Nutrition Examination Survey III and National Health Interview Survey. CONCLUSIONS: This study demonstrates a method for linking independently obtained health and job characteristic data based on occupational code.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".