Prevalence of Musculoskeletal Disorders Among Saskatchewan Farmers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The extent of the musculoskeletal disorder (MSD) problem is not well understood among Canadian farmers, and little too is known about their epidemiology. The purpose of this study was therefore to (1) determine the prevalence of MSDs among farmers in one Canadian province; and (2) describe the types and severities of these disorders and patterns in their occurrence. This cross-sectional analysis was conducted using baseline survey data from the Saskatchewan Farm Injury Cohort Study. Reports of MSDs, demographic and health-related variables, reports of farm-related injuries, and economic conditions of individual farms were available for 2595 adult participants from 1212 farms in Saskatchewan, Canada. Relationships between MSDs and time spent doing farm work were investigated using tests of association. The participation rate was 48.8%. Most (85.6%) of participants reported having musculoskeletal pain in at least one body part over the past year. The lower back was most frequently affected (57.7%), followed by shoulders (44.0%), and neck (39.6%). More serious pain prevented 27.9% of respondents from performing regular work activities. MSD prevalence did not vary by sex, commodity type, or by total hours of farm work completed; prevalence was significantly (P < .05) related to time spent performing biomechanically demanding tasks such as heavy lifting and working with arms overhead. The most common MSD site in farmers was the low back, followed by the upper and then lower extremities. Although this study aimed to identify high-risk groups, lack of differences between demographic groups suggests that the majority of farmers are at risk for MSDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it