Views of health system policymakers on the role of research in health policymaking in Israel
Bibliographic record
Abstract
BACKGROUND: The use of research evidence in health policymaking is an international challenge. Health systems, including that of Israel, are usually characterized by scarce resources and the necessity to make rapid policy decisions. Knowledge transfer and exchange (KTE) has emerged as a paradigm to start bridging the "know-do" gap. The purpose of this study was to explore the views of health system policymakers and senior executives involved in the policy development process in Israel regarding the role of health systems and policy research (HSPR) in health policymaking, the barriers and facilitators to the use of evidence in the policymaking process, and suggestions for improving the use of HSPR in the policymaking process. METHODS: A survey and an interview were verbally administered in a single face-to-face meeting with health system policymakers and senior executives involved in the policy development process in Israel. The data collection period was from July to October 2014. The potential participants included members of Knesset, officials from Israel's Ministry of Health, Ministry of Finance, health services organizations, and other stakeholder organizations (i.e., National Insurance Institute). The close-ended questions were based on previous surveys that had been conducted in this field. Interviews were tape recorded and transcribed. Descriptive statistics were conducted for close ended survey-questions and thematic analysis was conducted for open-ended interview questions. RESULTS: There were 32 participants in this study. Participants felt that the use of HSPR helps raise awareness on policy issues, yet the actual use of HSPR was hindered for many reasons. Facilitators do exist to support the use of HSPR in the policymaking process, such as a strong foundation of relationships between researchers and policymakers. However, many barriers exist such as the lack of relevance and timeliness of much of the currently available research to support decision-making and the paucity of funding to support research use. Suggestions to improve the use of HSPR focused on improving dissemination of research findings and ensuring that the research was more relevant and timely. CONCLUSIONS: This research demonstrated that health systems policymakers in Israel perceive having strong relationships and collaborations with researchers however there is room for improvement, e.g. partnering in research projects to ensure relevance and use. Furthermore, health system policymakers seem to be interested in receiving relevant research in a more useable format and are open to using research in decision making.
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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 | 0.235 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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, 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".