Risk Factors for Agricultural Injury: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
The objective of this study was to identify significant risk factors for agricultural injury based on the literature. The authors conducted a systematic review of commonly reported risk factors. Studies that reported adjusted odds ratio (OR) or relative risk (RR) estimates for the selected risk factors were identified from PubMed and Google Scholar. Pooled risk factor estimates were calculated using meta-analysis. A total of 441 (PubMed) and 285 (Google Scholar) studies were found in the initial searches; of these, 132 and 78 studies, respectively, met the selection criteria for injury outcomes, and 32 of these reported adjusted OR or RR estimates. One study was excluded because it did not meet the set Newcastle-Ottawa Scale quality criteria. Finally, 31 studies were used for meta-analysis. The pooled ORs for the risk factors were as follows: male gender (vs. female) 1.68, full-time farmer (vs. part-time) 2.17, owner/operator (vs. family member or hired worker) 1.64, regular medication use (vs. no regular medication use) 1.57, prior injury (vs. no prior injury) 1.75, health problems (vs. no health problems) 1.21, stress or depression (vs. no stress or depression) 1.86, and hearing loss (vs. no hearing loss) 2.01. All selected factors except health problems significantly increased the risk of injury, and they should be (a) considered when selecting high-risk populations for interventions, and (b) considered as potential confounders in intervention studies.
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.
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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".