Impact of Diabetes Mellitus on Occupational Health Outcomes in Canada
Bibliographic record
Abstract
BACKGROUND: Research suggests that diabetes mellitus (DM) has a negative impact on employment and workplace injury, but there is little data within the Canadian context. OBJECTIVE: To determine if DM has an impact on various occupational health outcomes using the Canadian Community Health Survey (CCHS). METHODS: CCHS data between 2001 and 2014 were used to assess the relationships between DM and various occupational health outcomes. The final sample size for the 14-year study period was 505 606, which represented 159 432 239 employed Canadians aged 15-75 years during this period. RESULTS: We found significant associations between people with diabetes and their type of occupation (business, finance, administration: 2009, p=0.002; 2010, p=0.002; trades, transportation, equipment: 2008, p=0.025; 2011, p=0.002; primary industry, processing, manufacturing, utility: 2013, p=0.018), reasons for missing work (looking for work: 2001, p=0.024; school or education: 2003, p=0.04; family responsibilities: 2014, p=0.015; other reasons: 2001, p<0.001; 2003, p<0.001; 2010, p=0.015), the number of work days missed (2010, 3 days, p=0.033; 4 days, p=0.038; 11 days, p<0.001; 24 days, p<0.001), and work-related injuries (traveling to and from work: 2014, p=0.003; working at a job or business: 2009, p=0.021; 2014, p=0.001). CONCLUSION: DM is associated with various occupational health outcomes, including work-related injury, work loss productivity, and occupation type. This allows stakeholders to assess the impact of DM on health outcomes in workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".