Bibliographic record
Abstract
［Aim］ This study aimed to clarify the education content and actual state of knowledge and skills training provided at each medical facility until flight nurses can practice independently. ［Subject and Method］ We requested participation in the questionnaire survey from medical institutions that operate medical helicopters in Japan. We analyzed 90 patients from 28 medical centers that participated in the study. ［Result］ Regarding flight nurse education in Japan, OJT was introduced in all the medical facilities surveyed. After beginning independent practice, flight nurses experienced exceptional cases such as disaster response and perinatal patient care. However, four （14.3％） facilities provided disaster-specific education；four （14.3％）, perinatal education；seven（25.0％），neonatal transportation； and seven （25.0％）, pediatric education. Six（21.4％） provided education on exceptional cases, which was less than a quarter of the total number of facilities. Fifteen facilities （53.6％） introduced simulation education. The duration of education varied from 10 days to ＞ 1 year. ［Conclusion］ Many facilities do not provide education on these particular cases as part of flight nurse education. Conversely, flight nurses are unlikely to experience exceptional cases during their OJT period, and flight nurses purportedly acquire this knowledge and skillset by participating in training sessions as well as self-improvement. Including exceptional cases during the OJT training period in the standard education program and incorporating simulation education at each facility are necessary.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".