Strategies to adapt and implement health system guidelines and recommendations: a scoping review
Bibliographic record
Abstract
BACKGROUND: Evidence-based health system guidelines are pivotal tools to help outline the important financial, policy and service components recommended to achieve a sustainable and resilient health system. However, not all guidelines are readily translatable into practice and/or policy without effective and tailored implementation and adaptation techniques. This scoping review mapped the evidence related to the adaptation and implementation of health system guidelines in low- and middle-income countries. METHODS: We conducted a scoping review following the Joanna Briggs Institute methodology for scoping reviews. A search strategy was implemented in MEDLINE (Ovid), Embase, CINAHL, LILACS (VHL Regional Portal), and Web of Science databases in late August 2020. We also searched sources of grey literature and reference lists of potentially relevant reviews. All findings were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. RESULTS: A total of 41 studies were included in the final set of papers. Common strategies were identified for adapting and implementing health system guidelines, related barriers and enablers, and indicators of success. The most common types of implementation strategies included education, clinical supervision, training and the formation of advisory groups. A paucity of reported information was also identified related to adaptation initiatives. Barriers to and enablers of implementation and adaptation were reported across studies, including the need for financial sustainability. Common approaches to evaluation were identified and included outcomes of interest at both the patient and health system level. CONCLUSIONS: The findings from this review suggest several themes in the literature and identify a need for future research to strengthen the evidence base for improving the implementation and adaptation of health system guidelines in low- and middle-income countries. The findings can serve as a future resource for researchers seeking to evaluate implementation and adaptation of health system guidelines. Our findings also suggest that more effort may be required across research, policy and practice sectors to support the adaptation and implementation of health system guidelines to local contexts and health system arrangements in low- and middle-income countries.
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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 | 0.151 | 0.338 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".