National Public Health Data Systems in the United States: Applications to Child Agricultural Injury Surveillance
Bibliographic record
Abstract
PURPOSE: The United States has no comprehensive national surveillance system for fatal or nonfatal child agricultural injuries. Thus, important knowledge gaps exist about recurrent injury patterns that could provide targeted focus for prevention efforts. The purpose of this study was to explore existing US public health data systems to determine their utility with respect to informing child agricultural injury surveillance and primary prevention. METHODS: Public health data systems were selected if they: (1) were national in scope, (2) were active and ongoing, (3) included physical injuries, and (4) contained a "farm" location variable. Data systems explored included National Emergency Medical Services Information System, National Trauma Data Bank, National Electronic Injury Surveillance System-All Injury Program, and National Vital Statistics System-Multiple Cause File. FINDINGS: Each data system contained substantial information per case with the number of fields ranging from 77 to 127. Beyond basic demographic information about the injured child, there were many injury descriptors, but few commonalities across systems. The most striking finding was the uniform absence of information on injury circumstances; that is, why and how the injury occurred, which is fundamental to planning and evaluating prevention initiatives. CONCLUSIONS: Although these public health data systems captured many details regarding medical aspects of the injury, they included little information on circumstances leading to injury, thus limiting their utility for child agricultural injury surveillance and primary prevention initiatives. We recommend any child agricultural injury data collection tool formally incorporate a structured narrative so underlying circumstances leading to injury events are documented.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".