Risk Factors for Low Back Disorders in Saskatchewan Farmers: Fieled-based Exposure Assessment to Build a Foundation for Epidemiological Studies
Bibliographic record
Abstract
BACKGROUND: Studies of many geographical settings and agricultural commodities show that low back disorders are an important public health issue among farmers, who represent a special rural population. However, few studies have examined the impact of low back disorders on farmers' work or the strategies that they adopt to avoid associated pain and disability. OBJECTIVE: This study protocol will investigate 3 issues related to low back disorders in Saskatchewan farmers: (1) the vibration, heavy lifting, and awkward postures farmers encounter during their work that might contribute to low back disorders; (2) the impact low back disorders have on farmers in terms of their ability to work; and (3) the types of preventative measures and solutions that farmers implement to reduce the occurrence of low back pain. METHODS: To answer these questions, researchers will travel to 30 farms to make measurements of vibration, lifting, and posture during the farmers' regular work tasks. Farmers will be interviewed about any pain and/or disability using standardized interview questions. Farmers will also be asked about safety measures they have implemented at their farm, such as modified tools or equipment, to reduce the occurrence of low back disorders or pain. RESULTS: Data collection is currently underway for this study, with the intention to complete all data collection and analysis by the end of 2018. CONCLUSIONS: Occupational determinants of health such as vibration, heavy lifting, and awkward postures are important in the development and progression of low back disorders, and the results of this study will allow for cost-effective epidemiological studies of these determinants in the future. In identifying prevention strategies, this study will also facilitate future research evaluating the effectiveness of safety measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.005 | 0.001 |
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".