The use of research in public health policy: a systematic review
Bibliographic record
Abstract
The use of robust research findings in public health policy has been strongly encouraged for bridging the evidence-policy gap. To assess and further promote evidence uptake, understanding how research evidence is being used by decision makers is very important. This systematic review examined primary studies exploring the use of research evidence in public health policy published between 2010 and January 2016; this work extended Orton et al’s (2011) review that covered studies published between 1980 and March 2010. The current systematic review incorporated 16 studies, representing 864 individuals, that provided insight into five topics pertaining to public health policy decision making: 1) the extent to which research evidence is used; 2) types of research evidence used; 3) the process of using research evidence; 4) factors other than research influencing decisions; and 5) barriers to and facilitators of evidence use. Relevant studies were identified using five different information sources including 14 electronic databases, websites of key organisations, forward citation search, reverse citation search, and internet search engines. Eligibility and methodological quality were assessed independently by two reviewers. The primary author conducted data extraction and the remaining authors reviewed the extraction results. Due to study heterogeneity, data were synthesised and findings were reported using a narrative approach. Findings aligned with previous literature to show that various types of research evidence are being accessed in public health policymaking. Further, challenges and enablers exist at multiple levels of the system, suggesting that use of research evidence is a complex, interdependent process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.159 | 0.399 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.043 | 0.042 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".