Facilitators, Barriers, and Strategies for Health-system Guidance Implementation: A Protocol for a Critical Interpretive Synthesis
Bibliographic record
Abstract
Abstract Background: As systematically developed statements about possible courses of action, health-system guidance (HSG) can assist with making decisions and developing policies to address problems or achieve goals in health systems. However, there are conceptual and methodological challenges related to HSG implementation due to the complexity of health systems and policymaking, the diversity of available evidence, and vast differences in contexts. To address these gaps, we aim to develop a theoretical framework for supporting the implementation of HSG as part of an effort to promote evidence-informed policymaking about health systems. Methods: To develop a theoretical framework about facilitators, barriers, and strategies to the implementation of HSG, we will apply a critical interpretive synthesis (CIS) approach to synthesize the findings from a range of relevant literature. We will search eleven electronic databases and grey literature websites to identify relevant published and grey literature. We will check the references of included studies and papers recommended by experts. Finally, we will conduct purposive searches to identify literature that fills any identified conceptual gaps. We will use relevance and a general five-items quality criteria to assess included papers. A standardized form will be developed for extracting information. We will use an interpretive analytic approach to synthesize the findings from included papers, including the constant comparative method throughout the analysis. The literature screening and relevance assessment will be conducted by two independent reviewers and disagreements will be resolved through discussion. Data extraction and synthesis will be extracted by the principal investigator and the sample of extracted data will be checked by a second reviewer for consistency and accuracy. Discussion: A new theoretical framework about facilitators, barriers, and strategies for HSG implementation will be developed using the CIS approach. The HSG implementation framework could be widely used for different HSG with varied topics and in different contexts (including low-, middle-, high-income countries and settings). In later work we will develop a tool for supporting HSG implementation based on the theoretical framework. Systematic review registration: PROSPERO CRDXXXX (Pending)
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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 | 0.261 | 0.319 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".