Musculoskeletal symptoms in tree planters in Ontario, Canada
Bibliographic record
Abstract
UNLABELLED: Tree planting is extremely physical, seasonal, repetitive work with high risk for musculoskeletal injuries. OBJECTIVES: (1) To assess musculoskeletal symptoms in tree planters as they develop over the course of the planting season. (2) To investigate the effect of pre-season level of physical activity on development of musculoskeletal symptoms. PARTICIPANTS: 132 tree planters from two reforestation camps participated in the study. METHODS: Three questionnaires were completed prior to the first work day of the planting season. Questionnaires included the International Physical Activity Questionnaire, a body map to report areas of musculoskeletal symptoms (MSS questionnaire), and a series of questions about planter demographics. A subset of study participants (n=14) also completed the MSS questionnaire each work shift during the planting season. Musculoskeletal symptoms in each area of the body were compared pre-and-post season using a paired t-test on data from the MSS questionnaire. RESULTS AND CONCLUSIONS: Areas of the body with the greatest amount of musculoskeletal pain and discomfort were the feet, wrists and back, whereas areas with the highest frequency of reported pain were the upper, middle and lower back. Musculoskeletal symptoms worsened significantly over the course of the work season. Pre-season level of physical activity could not be correlated with development of musculoskeletal symptoms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".