How can we improve guideline use? A conceptual framework of implementability
Bibliographic record
Abstract
BACKGROUND: Guidelines continue to be underutilized, and a variety of strategies to improve their use have been suboptimal. Modifying guideline features represents an alternative, but untested way to promote their use. The purpose of this study was to identify and define features that facilitate guideline use, and examine whether and how they are included in current guidelines. METHODS: A guideline implementability framework was developed by reviewing the implementation science literature. We then examined whether guidelines included these, or additional implementability elements. Data were extracted from publicly available high quality guidelines reflecting primary and institutional care, reviewed independently by two individuals, who through discussion resolved conflicts, then by the research team. RESULTS: The final implementability framework included 22 elements organized in the domains of adaptability, usability, validity, applicability, communicability, accommodation, implementation, and evaluation. Data were extracted from 20 guidelines on the management of diabetes, hypertension, leg ulcer, and heart failure. Most contained a large volume of graded, narrative evidence, and tables featuring complementary clinical information. Few contained additional features that could improve guideline use. These included alternate versions for different users and purposes, summaries of evidence and recommendations, information to facilitate interaction with and involvement of patients, details of resource implications, and instructions on how to locally promote and monitor guideline use. There were no consistent trends by guideline topic. CONCLUSIONS: Numerous opportunities were identified by which guidelines could be modified to support various types of decision making by different users. New governance structures may be required to accommodate development of guidelines with these features. Further research is needed to validate the proposed framework of guideline implementability, develop methods for preparing this information, and evaluate how inclusion of this information influences guideline use.
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 | 0.249 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.022 | 0.011 |
| Science and technology studies | 0.010 | 0.071 |
| Scholarly communication | 0.021 | 0.040 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".