Paper 4: a review of reporting and disseminating approaches for rapid reviews in health policy and systems research
Bibliographic record
Abstract
BACKGROUND: Transparent reporting of rapid reviews enables appropriate use of research findings and dissemination strategies can strengthen uptake and impact for the targeted knowledge users, including policy-makers and health system managers. The aim of this literature review was to understand reporting and dissemination approaches for rapid reviews and provide an overview in the context of health policy and systems research. METHODS: A literature review and descriptive summary of the reporting and disseminating approaches for rapid reviews was conducted, focusing on available guidance and methods, considerations for engagement with knowledge users, and optimizing dissemination. MEDLINE, PubMed, Google scholar, as well as relevant websites and reference lists were searched from January 2017 to March 2021 to identify the relevant literature with no language restrictions. Content was abstracted and charted. RESULTS: The literature review found limited guidance specific to rapid reviews. Building on the barriers and facilitators to systematic review use, we provide practical recommendations on different approaches and methods for reporting and disseminating expedited knowledge synthesis considering the needs of health policy and systems knowledge users. Reporting should balance comprehensive accounting of the research process and findings with what is "good enough" or sufficient to meet the requirements of the knowledge users, while considering the time and resources available to conduct a review. Typical approaches may be used when planning the dissemination of rapid review findings; such as peer-reviewed publications or symposia and clear and ongoing engagement with knowledge users in crafting the messages is essential so they are appropriately tailored to the target audience. Consideration should be given to providing different products for different audiences. Dissemination measures and bibliometrics are also useful to gauge impact and reach. CONCLUSIONS: Limited guidance specific to the reporting and dissemination of rapid reviews is available. Although approaches to expedited synthesis for health policy and systems research vary, considerations for the reporting and dissemination of findings are pertinent to all.
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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 | 0.665 | 0.846 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.049 | 0.056 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.036 | 0.034 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".