Exploring Policy Change in the Emergency Department: A Qualitative Approach to Understanding Local Policy Creation and the Barriers to Implementing Change
Bibliographic record
Abstract
Introduction With thousands of new medical trials released every year, health care policymakers must work diligently to incorporate new evidence into clinical practice. Although there are some broad conceptual frameworks for knowledge translation in the emergency department (ED), there are few user-centered studies that illustrate how local policymakers develop and disseminate new policies. Objectives Our study sought to evaluate the process by which new departmental policies are formed in ED, how new evidence was integrated into this process, and to explore barriers to implementation. Methods Semi-structured interviews were conducted with local administrators from nine major hospitals in Ontario, Canada. Interviews were transcribed and qualitative data was analyzed using constructivist grounded theory. Results Five broad steps in the policy creation process were identified: 1) Problem identification and motivation for change; 2) building a policy team; 3) policy construction; 4) implementation and monitoring of new departmental policies; 5) actively addressing barriers to the ED policymaking process. Common sub-themes in each of these categories were highlighted. Four main themes also emerged regarding barriers experienced in policymaking: Education and knowledge transfer; lack of a change culture; resource limitations; and cumbersome bureaucratic structures. Conclusion Our study identified common facilitators and barriers that policymakers face in their ability to create health policy in the ED. While local context influences the policymaking process, a standardized framework would ensure a more systematic approach for policymakers and allow scientists to better understand how evidence is integrated at the local level.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".