Public Health Considerations in Knowledge Translation in the Emergency Department
Bibliographic record
Abstract
Effective preventive and screening interventions have not been widely adopted in emergency departments (EDs). Barriers to knowledge translation of these initiatives include lack of knowledge of current evidence, perceived lack of efficacy, and resource availability. To address this challenge, the Academic Emergency Medicine 2007 Consensus Conference, "Knowledge Translation in Emergency Medicine: Establishing a Research Agenda and Guide Map for Evidence Uptake," convened a public health focus group. The question this group addressed was "What are the unique contextual elements that need to be addressed to bring proven preventive and other public health initiatives into the ED setting?" Public health experts communicated via the Internet beforehand and at a breakout session during the conference to reach consensus on this topic, using published evidence and expert opinion. Recommendations include 1) to integrate proven public health interventions into the emergency medicine core curriculum, 2) to configure clinical information systems to facilitate public health interventions, and 3) to use ancillary ED personnel to enhance delivery of public health interventions and to obtain successful funding for these initiatives. Because additional research in this area is needed, a research agenda for this important topic was also developed. The ED provides medical care to a unique population, many with increased needs for preventive care. Because these individuals may have limited access to screening and preventive interventions, wider adoption of these initiatives may improve the health of this vulnerable population.
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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 | 0.527 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.030 | 0.038 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".