Evaluation of the North American Guidelines for Children’s Agricultural Tasks using a case series of injuries
Bibliographic record
Abstract
OBJECTIVE: To evaluate the potential for the North American Guidelines for Children's Agricultural Tasks (NAGCAT) to prevent the occurrence of pediatric farm injuries. This evaluation focuses upon farm injuries experienced when children were engaged in farm work. DESIGN: Novel outcome evaluation involving primary review of three retrospective case series. SETTING: Fatal, hospitalized, and restricted activity injuries from the United States and Canada. SUBJECTS: Nine hundred and thirty four pediatric farm injury cases. METHODS: The applicability of NAGCAT to each case was rated. For injuries where NAGCAT were applicable, recurrent injury patterns were described and the potential for NAGCAT to prevent their occurrence was assessed. RESULTS: A total of 283 (30.3%) cases involved children engaged in farm work. There was an applicable NAGCAT guideline in 64.9% of the work related cases. Leading individual guidelines applicable to the injury events were: (1) working with large animals; (2) driving a farm tractor; and (3) farm work with an all-terrain vehicle. In the judgment of the research team, 59.6% of these injuries were totally preventable if the principles espoused by NAGCAT had been applied. CONCLUSIONS: NAGCAT are a set of consensus guidelines aimed at the prevention of pediatric farm injuries. The findings suggest that NAGCAT, if applied, would be efficacious in preventing many of the most serious injuries experienced by children engaged in farm work. However, work related injuries represent only a modest portion of pediatric farm injuries. This new information assists in the refinement of NAGCAT as an injury control resource and puts its potential efficacy into context.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".