Do guidelines offer implementation advice to target users? A systematic review of guideline applicability
Bibliographic record
Abstract
OBJECTIVE: Providers and patients are most likely to use and benefit from guidelines accompanied by implementation support. Guidelines published in 2007 and earlier assessed with the Appraisal of Guidelines, Research and Evaluation (AGREE) instrument scored poorly for applicability, which reflects the inclusion of implementation instructions or tools. The purpose of this study was to examine the applicability of guidelines published in 2008 or later and identify factors associated with applicability. DESIGN: Systematic review of studies that used AGREE to assess guidelines published in 2008 or later. DATA SOURCES: MEDLINE and EMBASE were searched from 2008 to July 2014, and the reference lists of eligible items. Two individuals independently screened results for English language studies that reviewed guidelines using AGREE and reported all domain scores, and extracted data. Descriptive statistics were calculated across all domains. Multilevel regression analysis with a mixed effects model identified factors associated with applicability. RESULTS: Of 245 search results, 53 were retrieved as potentially relevant and 20 studies were eligible for review. The mean and median domain scores for applicability across 137 guidelines published in 2008 or later were 43.6% and 42.0% (IQR 21.8-63.0%), respectively. Applicability scored lower than all other domains, and did not markedly improve compared with guidelines published in 2007 or earlier. Country (UK) and type of developer (disease-specific foundation, non-profit healthcare system) appeared to be associated with applicability when assessed with AGREE II (not original AGREE). CONCLUSIONS: Despite increasing recognition of the need for implementation tools, guidelines continue to lack such resources. To improve healthcare delivery and associated outcomes, further research is needed to establish the type of implementation tools needed and desired by healthcare providers and consumers, and methods for developing high-quality tools.
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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 | 0.047 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".