Accident prevention activities: A national survey of health authorities
Bibliographic record
Abstract
Objective To explore how health authorities in England are participating in the planning and development of accident prevention initiatives, particularly in relation to their partnership work. Design Postal questionnaire. Setting All health authorities in England. Method A questionnaire was sent to the director of health promotion/ lead officer of all the health authorities in England. They were asked for information about priorities, strategies, data collection and joint working. Results A response rate of 93 per cent was achieved. Sixty-eight per cent of districts had accidents as one of their top five priorities. A quarter (25 per cent) of districts did not have an accident prevention strategy. The majority (81 per cent), of districts were dissatisfied with their current position in relation to the collection of data. Recent changes in national policy were seen as positive to accident prevention work. However, lack of resources was seen as an important barrier. Conclusion There appears to be some considerable differences in the activities of health authorities, particularly in relation to partnership work and accident prevention strategies. Recommendations to emerge include the need for national action in relation to the collection of data, and the need for further investigation into the resource issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".