Bibliographic record
Abstract
OBJECTIVES: To examine the association between education-to-job mismatch and work injury. METHODS: Cross-sectional data from the 2003 and 2005 Canadian Community Health Surveys (n=63,462) were used to examine the relationship between having an educational level that is incongruent with occupational skills requirements and the risk of sustaining a work injury requiring medical attention or a work-related repetitive movement injury (RMI). The effect on injury of the interaction of overeducation with recent immigrant status was also examined. Models were stratified by sex and adjusted for possible confounders. Occupational physical demands were conceptualised as a potential mediating variable. RESULTS: After adjustment for covariates, over-education was associated with work injury and RMI for both sexes. Adjustment for occupational demands attenuated the impact on work injury but did not eliminate the effect on RMI among men. The interaction of over-education and recent immigrant status resulted among men in a fourfold increase in the odds of work injury compared with non-recent immigrants who were not over-educated. After adjustment for occupational demands, over-educated recent immigrant men still had more than a twofold increase in the odds of injury. CONCLUSIONS: The risk of sustaining a work injury is higher among those whose education exceeds that of job requirements. These findings highlight the need to address barriers to suitable employment, particularly among recent immigrants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".