Review and Meta-analysis of Emerging Risk Factors for Agricultural Injury
Bibliographic record
Abstract
Agricultural injury is a significant public health problem globally. Extensive research has addressed this problem, and a growing number of risk factors have been reported. The authors evaluated the evidence for frequently reported risk factors earlier. The objective in the current study was to identify emerging risk factors for agricultural injury and calculate pooled estimates for factors that were assessed in two or more studies. A total of 441 (PubMed) and 285 (Google Scholar) studies were identified focusing on occupational injuries in agriculture. From these, 39 studies reported point estimates of risk factors for injury; 38 of them passed the Newcastle-Ottawa criteria for quality and were selected for the systematic review and meta-analysis. Several risk factors were significantly associated with injury in the meta-analysis. These included older age (vs. younger), education up to high school or higher (vs. lower), non-Caucasian race (vs. Caucasian), Finnish language (vs. Swedish), residence on-farm (vs. off-farm), sleeping less than 7–7.5 hours (vs. more), high perceived injury risk (vs. low), challenging social conditions (vs. normal), greater farm sales, size, income, and number of employees on the farm (vs. smaller), animal production (vs. other production), unsafe practices conducted (vs. not), computer use (vs. not), dermal exposure to pesticides and/or chemicals (vs. not), high cooperation between farms (vs. not), and machinery condition fair/poor (vs. excellent/good). Eighteen of the 25 risk factors were significant in the meta-analysis. The identified risk factors should be considered when designing interventions and selecting populations at high risk of injury.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".