Implementing clinical guidelines: Current evidence and future implications
Bibliographic record
Abstract
One of the most common findings from health services research is a failure to routinely translate research findings into daily practice. Previous systematic reviews of strategies to promote the uptake of research findings suffered from a range of methodologic problems that have been addressed in a more recent systematic review of guideline dissemination and implementation strategies. Changes in practitioner behavior; in the desired direction, were reported in 86% of the comparisons made. The median effect size overall was approximately 10% improvement in absolute terms. The review suggests that interventions that were previously thought to be ineffective (e.g., dissemination of educational materials) may have modest but worthwhile benefits. Also, multifaceted interventions, previously thought to be more effective than single interventions, were found to be no more effective than single interventions. Overall, there is an imperfect evidence base for decision makers to work from. Many studies had methodologic weaknesses, and reporting of this kind of research is generally poor, making the generalizability of study findings frequently uncertain. A better theoretical underpinning of studies would make this body of research more useful.
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 | 0.058 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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, 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".