Essential skills for using research evidence in public health policy: a systematic review
Bibliographic record
Abstract
Background: Decisions related to the development and implementation of public health programmes or policies can benefit from more effective use of the best available knowledge. However, decision makers do not always feel sufficiently equipped or may lack the capacity to use evidence. This can lead them to overlook or set aside research results that could be relevant to their practice area. Aims and objectives: The objective of this systematic review was to synthesise the essential skills that facilitate the use of research evidence by public health decision makers. Methods: Thirty-nine articles that met our inclusion criteria were included. An inductive approach was used to extract data on evidence-informed decision-making-related skills and data were synthesised as a narrative review. Findings: The analysis revealed three categories of skills that are essential for evidence-informed decision-making process: interpersonal, cognitive , and leadership and influencing skills . Such cross-sectoral skills are essential for identifying, obtaining, synthesising, and integrating sound research results into the decision-making process. Discussion and conclusions: The results of this systematic review will help direct capacity-building efforts towards enhancing research evidence use by public health decision makers, such as developing different types of training that would be relevant to their needs. Also, when considering the evidence-informed decision-making skills development, there are several useful and complementary approaches to link research most effectively to action. On one hand, it is important not only to support decision makers at the individual level through skills development, but also to provide them with a day-to-day environment that is conducive to evidence use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".