The Relationship Between Overqualification and Incident Diabetes: A 14-Year Follow-Up Study
Bibliographic record
Abstract
OBJECTIVE: Recent research identified that workplace factors play a role in the development of diabetes mellitus (DM). This study examines the longitudinal association of work-related overqualification with the incidence of DM over a 14-year follow-up period. METHODS: We used data from the 2003 Canadian Community Health Survey linked to the Ontario Health Insurance Plan and the Canadian Institute for Health Information Discharge Abstract databases. Cox proportional hazards regression models were performed to evaluate the relationship between overqualification and the incidence of DM. RESULTS: Over the study period, there were 91,835 person-years of follow-up (median follow-up = 13.7 years). The final sample included 7026 respondents (mean [standard deviation] age at baseline = 47.1 [8.2]; 47% female). An elevated risk of DM was associated with substantial overqualification (hazard ratio = 1.58, 95% confidence interval = 1.01-2.49) after adjustment for sociodemographic, health, and work variables. Additional adjustment for body mass index and health behaviors attenuated this risk (hazard ratio = 1.30, 95% confidence interval = 0.81-2.08). Underqualification was not associated with the incidence of DM in adjusted regression models. We did not observe any statistical difference in the effects of overqualification on DM risk across sex or education groups. CONCLUSIONS: This study adds to the growing body of research literature uncovering the relationships between work exposures and DM risk. The results from the study suggest that higher body mass index and, to a lesser extent, health behaviors may be mediating factors in the association between overqualification and incident DM. Further research on the association of overqualification with DM is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".