Interventions encouraging the use of systematic reviews by health policymakers and managers: A systematic review
Bibliographic record
Abstract
BACKGROUND: Systematic reviews have the potential to inform decisions made by health policymakers and managers, yet little is known about the impact of interventions to increase the use of systematic reviews by these groups in decision making. METHODS: We systematically reviewed the evidence on the impact of interventions for seeking, appraising, and applying evidence from systematic reviews in decision making by health policymakers or managers. Medline, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Health Technology Assessment Database, and LISA were searched from the earliest date available until April 2010. Two independent reviewers selected studies for inclusion if the intervention intended to increase seeking, appraising, or applying evidence from systematic reviews by a health policymaker or manager. Minimum inclusion criteria were a description of the study population and availability of extractable data. RESULTS: 11,297 titles and abstracts were reviewed, leading to retrieval of 37 full-text articles for assessment; four of these articles met all inclusion criteria. Three articles described one study where five systematic reviews were mailed to public health officials and followed up with surveys at three months and two years. The articles reported from 23% to 63% of respondents declaring they had used systematic reviews in policymaking decisions. One randomised trial indicated that tailored messages combined with access to a registry of systematic reviews had a significant effect on policies made in the area of healthy body weight promotion in health departments. CONCLUSIONS: The limited empirical data renders the strength of evidence weak for the effectiveness and the types of interventions that encourage health policymakers and managers to use systematic reviews in decision making.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.427 | 0.651 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.052 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".