Barriers and facilitators to uptake of systematic reviews by policy makers and health care managers: a scoping review
Bibliographic record
Abstract
BACKGROUND: We completed a scoping review on the barriers and facilitators to use of systematic reviews by health care managers and policy makers, including consideration of format and content, to develop recommendations for systematic review authors and to inform research efforts to develop and test formats for systematic reviews that may optimise their uptake. METHODS: We used the Arksey and O'Malley approach for our scoping review. Electronic databases (e.g., MEDLINE, EMBASE, PsycInfo) were searched from inception until September 2014. Any study that identified barriers or facilitators (including format and content features) to uptake of systematic reviews by health care managers and policy makers/analysts was eligible for inclusion. Two reviewers independently screened the literature results and abstracted data from the relevant studies. The identified barriers and facilitators were charted using a barriers and facilitators taxonomy for implementing clinical practice guidelines by clinicians. RESULTS: We identified useful information for authors of systematic reviews to inform their preparation of reviews including providing one-page summaries with key messages, tailored to the relevant audience. Moreover, partnerships between researchers and policy makers/managers to facilitate the conduct and use of systematic reviews should be considered to enhance relevance of reviews and thereby influence uptake. CONCLUSIONS: Systematic review authors can consider our results when publishing their systematic reviews. These strategies should be rigorously evaluated to determine impact on use of reviews in decision-making.
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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 | 0.649 | 0.837 |
| Meta-epidemiology (narrow) | 0.003 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.042 | 0.044 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".