Effectiveness of implementation strategies in improving physician adherence to guideline recommendations in heart failure: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The uptake of Clinical Practice Guideline (CPG) recommendations that improve outcomes in heart failure (HF) remains suboptimal. We will conduct a systematic review to identify implementation strategies that improve physician adherence to class I recommendations, those with clear evidence that benefits outweigh the risks. We will use American, Canadian and European HF guidelines as our reference. METHODS AND ANALYSIS: We will conduct a literature search in the databases of MEDLINE, EMBASE, HEALTHSTAR, CINAHL, Cochrane Library, Campbell Collaboration, Joanna Briggs Institute Evidence Based Practice, Centre for Reviews and Dissemination and Evidence Based Practice Centres. We will include prospective studies evaluating implementation interventions aimed at improving uptake of class I CPG recommendations in HF. We will extract data in duplicate. We will classify interventions according to their level of application (ie, provider, organisation, systems level) and common underlying characteristics (eg, education, decision-support, financial incentives) using the Cochrane Effective Practice and Organisation of Care Taxonomy. We will assess the impact of the intervention on adherence to the CPGs. Outcomes will include proportion of eligible patients who were: prescribed a CPG-recommended pharmacological treatment; referred for device consideration; provided self-care education at discharge; and provided left ventricular function assessment. We will include clinical outcomes such as hospitalisations, readmissions and mortality, if data is available. We will identify the common elements of successful and failing interventions, and examine the context in which they were applied, using the Process Redesign contextual framework. We will synthesise the results narratively and, if appropriate, will pool results for meta-analysis. DISCUSSION AND DISSEMINATION: In this review, we will assess the impact of implementation strategies and contextual factors on physician adherence to HF CPGs. We will explore why some interventions may succeed in one setting and fail in another. We will disseminate our findings through briefing reports, publications and presentations. TRIAL REGISTRATION NUMBER: CRD42015017155.
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 | 0.095 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.007 |
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".