What we are not talking about: An evaluation of prevention messaging in print media reporting on agricultural injuries and fatalities
Bibliographic record
Abstract
BACKGROUND: Agricultural injury and fatality pose a significant burden on farmers, families, health care systems, and economies. One way of increasing knowledge of this problem and promoting prevention is the use of printed mass media such as newspapers. METHODS: We conducted a scan of all media reports contained in the Canadian Agricultural Safety Association (CASA) archives for the period January, 2007 to September, 2009, inclusive, for injury and fatality and analyzed newspaper articles for prevention messages. RESULTS: Of the 409 articles in the database, 392 met the inclusion criteria. Ninety-three of the articles (24%) contained a prevention message, and 39 (10%) of these were considered to be strong. Urban papers were two times more likely to have a safety message (OR = 2.03) while adult-related events were less likely to have a safety message included (OR = 0.49). CONCLUSION: Print media reporting of agricultural injury and fatality represents a missed opportunity to provide a prevention message. More can be done to improve linkages between news media outlets and injury prevention specialists to improve prevention content in newsprint.
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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 | 0.057 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".