A Profile of Farmers and Other Employed Canadians With Chronic Back Pain: A Population‐Based Analysis of the 2009‐2010 Canadian Community Health Surveys
Bibliographic record
Abstract
PURPOSE: Chronic back disorders (CBDs) are a serious public health issue, both in the general population and among farmers. However, it is not clear whether all individuals with CBD should be treated the same, or if some subpopulations have special needs. This study's purpose was to determine the demographic, socioeconomic, co-morbidity, and other health characteristics of Canadian farmers and nonfarmers with self-reported CBD. METHODS: We performed a secondary analysis of the 2009-2010 Canadian Community Health Survey to develop a profile of adults with CBD comparing farmers (N = 350) to nonfarmer employed persons (N = 11,251). In addition to descriptive analysis, multiple logistic regression was used to control for possible confounding. FINDINGS: Our results indicate that farmers with CBD are significantly more likely to be older, less educated, and more often male and living rurally than nonfarmers with CBD. We found no difference between rates and type of co-morbidities between farmers and nonfarmers. However, the sociodemographic differences between farmers and nonfarmers with CBD may impact the design of effective interventions and have implications for health services planning and health care delivery. The information presented is anticipated to help address the identified need for musculoskeletal disorder prevention in agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".