Nurses' Agricultural Education in the Southeastern United States
Bibliographic record
Abstract
The number of nurses across the United States with expertise in agricultural health nursing is unknown, yet as many as 8,000 are needed. This article describes agricultural health content in nursing programs in the southeastern United States. Agriculture is primarily family based but ranks among the top three most hazardous industries in America. Nurses in the southeastern United States serve more than 541,000 farm families, more than a quarter of the nation's agricultural population. A 15-item survey was mailed to 185 nursing schools located within 13 southeastern states. Information was requested about undergraduate and graduate curricula that included information about agricultural health and safety. Surveys were returned from 113 programs (61.1%). Schools with larger percentages of rural students were more likely to include mention of agricultural health; however, scant attention was given to any rurally focused content. In 27.4% of the schools, no mention of agricultural health issues was made, and 54.0% of nursing faculty who completed the survey were not aware of the need for nurses with agricultural health expertise. Results suggested that, when agricultural health topics were presented in class, student interest in the topic increased. Given the occupational hazards faced in agriculture and the region's economic dependence on agriculture, increased attention should be focused on agricultural health content within nursing programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".