Survey of preferred guideline attributes: what helps to make guidelines more useful for emergency health practitioners?
Bibliographic record
Abstract
Background: Enhancing CPG acceptance and implementation can play a major role in the development and establishment of emergency medicine as a specialty in many parts of the world. A Guideline International Network special interest group established to support collaboration to improve uptake of clinical practice guidelines (CPGs) across the emergency care sector conducted an international survey to identify attributes of guideline likely to enhance their use. Methods: A Web-based survey was undertaken to determine how CPGs were accessed, the preferred formats and attributes of guidelines, and familiarity with GRADE. The criteria used to identify preferred attributes of guidelines were adapted from the AGREE II Tool. Results: Two hundred six responses were received from 31 countries, 74/206 (36%) from the US, 28/206 (16%) from Canada, 17/206 (8%) from Australia and 15/206 (7%) from the UK. The majority of responses were from physicians (176/206, 85%) with 15/206 (7%) of responses from nurses and 9/206 (4%) from pre-hospital emergency services personnel. The preferred format for guidelines was clinical protocols that incorporated recommendations into workflow, and the most preferred attribute of guidelines was the clear identification of key recommendations. The results also identified that within the group that responded to the question related to GRADE, 66% were unfamiliar with this system for summarizing evidence in relationship to recommendations. Conclusions: The findings provide the basis for further research to explore the most appropriate formats for guidelines or guidelines resources tailored to the needs of the emergency care providers.
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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 | 0.025 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".