How is the use of research evidence in health policy perceived? A comparison between the reporting of researchers and policy-makers
Bibliographic record
Abstract
BACKGROUND: The use of health policy and systems research (HPSR) to inform health policy-making is an international challenge. Incorporating HPSR into decision-making primarily involves two groups, namely researchers (knowledge producers) and policy-makers (knowledge users). The purpose of this study was to compare the perceptions of Israeli health systems and policy researchers and health services policy-makers regarding the role of HPSR, factors influencing its uses and potential facilitators and barriers to HPSR, and implementation of knowledge transfer and exchange (KTE) activities. METHODS: test. RESULTS: A total of 37 researchers and 32 policy-makers responded to the survey. While some views were in alignment, others showed differences. More policy-makers than researchers perceived that the use of HPSR in policy was hindered by practical implementation constraints, whereas more researchers felt that its use was hindered by a lack of coordination between knowledge producers and users. A larger percentage of policy-makers, as compared to researchers, reported that facilitators to the KTE process are in place and a larger percentage of researchers perceived barriers within the KTE environment. A larger percentage of policy-makers perceived KTE activities were in place as compared to researchers. Results also showed large differences in the perceptions of the two groups regarding policy formulation and which organisations they perceived as exerting strong influence on policy-making. CONCLUSIONS: This research demonstrated that there are differences in the perceptions of knowledge producers and users about the process of KTE. Future work should focus on minimising the challenges highlighted here and implementing new KTE activities. These activities could include making the researchers aware of the most effective manner in which to package their results, providing training to policy-makers and assuring that policy-makers have technical access to appropriate databases to search for HPSR. These results underscore the need for the groups to communicate and clarify to each other what they can offer and what they require.
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 | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.534 | 0.751 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".