“More bang for the buck”: exploring optimal approaches for guideline implementation through interviews with international developers
Bibliographic record
Abstract
BACKGROUND: Population based studies show that guidelines are underused. Surveys of international guideline developers found that many do not implement their guidelines. The purpose of this research was to interview guideline developers about implementation approaches and resources. METHODS: Semi-structured telephone interviews were conducted with representatives of guideline development agencies identified in the National Guideline Clearinghouse and sampled by country, type of developer, and guideline clinical indication. Participants were asked to comment on the benefits and resource implications of three approaches for guideline implementation that varied by responsibility: developers, intermediaries, or users. RESULTS: Thirty individuals from seven countries were interviewed, representing government (n = 12) and professional (n = 18) organizations that produced guidelines for a variety of clinical indications. Organizations with an implementation mandate featured widely inconsistent funding and staffing models, variable approaches for choosing promotional strategies, and an array of dissemination activities. When asked to choose a preferred approach, most participants selected the option of including information within guidelines that would help users to implement them. Given variable mandate and resources for implementation, it was considered the most feasible approach, and therefore most likely to have impact due to potentially broad use. CONCLUSIONS: While implementation approaches and strategies need not be standardized across organizations, the findings may be used by health care policy makers and managers, and guideline developers to generate strategic and operational plans that optimize implementation capacity. Further research is needed to examine how to optimize implementation capacity by guideline developers, intermediaries and users.
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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 | 0.103 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".