Agriculture-related Injuries: Discussion in Canadian Media
Bibliographic record
Abstract
Objectives: This study examined news media reporting on farm injuries in Canada for the occurrence of prevention messages and factors related to whether an event was reported in more than one article.Methods: This study used a media database maintained by the Canadian Agricultural Safety Association (CASA), which stores publicly available news media reports of agricultural injuries and fatalities in Canada. Media reports were obtained for the years 2010 through 2017. Reports were coded as whether they reported a fatal or non-fatal injury, age and gender of those affected, urban or rural media, as well as whether they involved machinery, or were in French. Logistic regression was used to determine which variables predicted an event being reported more than once, and whether a report included a prevention message.Results: The database identified 856 relevant articles. Only 6.3% of the articles included a prevention message, and 34.7% were duplicate articles. Fatal injuries were more likely to be reported in multiple articles (odds ratio: 2.44). There was also significant variation in the occurrence of multiple reports across the years of the study. Prevention messages were more likely to occur when at least one child or female victim was involved in an event. However, only year of publication remained significantly associated with the occurrence of a prevention message in multivariable regression (odds ratio: 0.85).Conclusion: Prevention messages are rare in media reporting of farm injuries and are decreasing over time. Improved reporting is needed to aid in farm injury prevention.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".