Integrating guideline development and implementation: analysis of guideline development manual instructions for generating implementation advice
Bibliographic record
Abstract
BACKGROUND: Guidelines are important tools that inform healthcare delivery based on best available research evidence. Guideline use is in part based on quality of the guidelines, which includes advice for implementation and has been shown to vary. Others hypothesized this is due to limited instructions in guideline development manuals. The purpose of this study was to examine manual instructions for implementation advice. METHODS: We used a directed and summative content analysis approach based on an established framework of guideline implementability. Six manuals identified by another research group were examined to enumerate implementability domains and elements. RESULTS: Manuals were similar in content but lacked sufficient detail in particular domains. Most frequently this was Accomodation, which includes information that would help guideline users anticipate and/or overcome organizational and system level barriers. In more than one manual, information was also lacking for Communicability, information that would educate patients or facilitate their involvement in shared decision making, and Applicability, or clinical parameters to help clinicians tailor recommendations for individual patients. DISCUSSION: Most manuals that direct guideline development lack complete information about incorporating implementation advice. These findings can be used by those who developed the manuals to consider expanding their content in these domains. It can also be used by guideline developers as they plan the content and implementation of their guidelines so that the two are integrated. New approaches for guideline development and implementation may need to be developed. Use of guidelines might be improved if they included implementation advice, but this must be evaluated through ongoing research.
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 | 0.146 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".