The Use of Health Care Policy to Facilitate Evidence-based Knowledge Translation in Emergency Medicine
Bibliographic record
Abstract
Health care policy can facilitate emergency medicine knowledge translation (KT). Because of this, the 2007 Academic Emergency Medicine Consensus Conference on KT identified a specific theme regarding issues of health care policy and KT. Six months before the Consensus Conference, international experts in the area were invited to communicate on health care policies regarding all areas of KT via e-mail and "Google groups." From this communication, and using available evidence, specific recommendations and research questions were developed. At the Consensus Conference, additional comments were incorporated. This report summarizes the results of this collaborative effort and provides a set of recommendations and accompanying research questions to guide development, implementation, and evaluation of health care policies intended to promote KT in emergency medicine. The recommendations are to 1a) involve appropriate stakeholders in the health care policy process; 1b) collaborate with policy makers when health care policy focus areas are being developed; 2) use previously validated clinical practice guideline development tools; 3) address implementation issues during the development of health care policies; 4) monitor outcomes with performance measures appropriate to different practice environments; and 5) plan periodic reviews to uncover new clinical evidence, new methods to improve KT, and new technologies. To advance the further development of these recommendations, a research agenda is proposed.
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 | 0.631 | 0.669 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".