Facilitators, barriers and strategies for health-system guidance implementation: a critical interpretive synthesis protocol
Bibliographic record
Abstract
Abstract Background As systematically developed statements regarding possible courses of action, health system guidance (HSG) can assist with making decisions about addressing problems or achieving goals in health systems. However, there are conceptual and methodological challenges in HSG implementation due to the complexity of health-system policy-making, the diversity of available evidence and vast differences in contexts. To address these gaps, we aim to develop a theoretical framework for supporting HSG implementation as part of a broader effort to promote evidence-informed policy-making in health systems. Methods To develop a theoretical framework about facilitators, barriers and strategies for HSG implementation, we will apply a critical interpretive synthesis (CIS) approach to synthesize the findings from a range of relevant literature. We will search 11 electronic databases and seven organizational websites to identify relevant published and grey literature. We will check the references of included studies and contact experts to identify additional eligible papers. Finally, we will conduct purposively sampling of the literature to fill any identified conceptual gaps. We will use relevance and five 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, including a constant comparative method throughout the analysis. Two independent reviewers will conduct the literature screening and relevance assessment, and disagreements will be resolved through discussion. The principal investigator will conduct data extraction and synthesis, and a second reviewer will check the sample of extracted data for consistency and accuracy. Discussion A new theoretical framework about facilitators, barriers and strategies for HSG implementation will be developed using a CIS approach. The HSG implementation framework could be widely used for supporting the implementation of HSG covering varied topics and in different contexts (including low-, middle- and high-income countries). In later work, we will develop a tool for supporting HSG implementation based on the theoretical framework. Registration PROSPERO CRD42020214072. Date of Registration: 14 December 2020.
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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 | 0.292 | 0.385 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".