063 Enhancing the Uptake of Clinical Practice Guidelines: The Development of a Guideline Implementability Tool (Guide-It)
Bibliographic record
Abstract
Background Guidelines have the potential to facilitate implementation of evidence into practice but this has not been consistently achieved. We developed a guideline implementability tool (GUIDE-IT), which can assess the implementability of guideline recommendations. Objective To determine if GUIDE-IT can improve the Language and Format of guideline recommendations. Methods Using a mixed-methods approach to develop GUIDE-IT, we conducted 1) a Realist Review of guideline factors influencing uptake, and used its results to build a conceptual model of guideline implementability; 2) qualitative interviews with 20 family physicians to determine factors influencing guideline uptake and to obtain input on tool design; 3) created a prototype and conducted validity assessments with experts in guideline development and human factors. GUIDE-IT was then pilot tested with the Canadian Diabetes/Paediatric Associations (CDA, CPS) to determine its potential for assessing the implementability of guideline recommendations. Results Pilot testing with CDA and CPS developers showed that factors across 4 sub-domains of Language (clarity, simplicity, specificity, and actionability) and 3 sub-domains of Format (presentation, components, and multiple versions) were applicable for modifying recommendations. GUIDE-IT was feasible to use by guideline developers to identify implementability problems and to improve recommendations. Discussion GUIDE-IT is based on a robust evidentiary base with the potential to improve guidelines. Next steps include evaluating GUIDE-IT in a controlled trial to determine its impact on end-user clinical decision making. Implications for Guideline Developers/Users GUIDE-IT has potential to be a practical tool for developers to improve the language and format of guideline recommendations.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.126 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".