Examining the use of health systems and policy research in the health policymaking process in Israel: views of researchers
Bibliographic record
Abstract
BACKGROUND: All too often, health policy and management decisions are made without making use of or consulting with the best available research evidence, which can lead to ineffective and inefficient health systems. One of the main actors that can ensure the use of evidence to inform policymaking is researchers. The objective of this study is to explore Israeli health systems and policy researchers' views and perceptions regarding the role of health systems and policy research (HSPR) in health policymaking and the barriers and facilitators to the use of evidence in the policymaking process. METHODS: A survey of researchers who have conducted HSPR in Israel was developed. The survey consisted of a demographics section and closed questions, which focused on support both within the researchers' organisations and the broader environment for KTE activities, perceptions on the policymaking process, and the potential influencing factors on the process. The survey was sent to all health systems and policy researchers in Israel from academic institutions, hospital settings, government agencies, the four health insurance funds, and research institutes (n = 107). All responses were analyzed using descriptive statistics. For close-ended questions about level of agreement we combined together the two highest categories (agree or strongly agree) for analysis. RESULTS: Thirty-seven respondents participated in the survey. While many respondents felt that the use of HSPR may help raise awareness on policy issues, the majority of respondents felt that the actual use of HSPR was hindered for many reasons. While facilitators do exist to support the use of research evidence in policymaking, numerous barriers hinder the process such as challenges in government/provider relations, policymakers lacking the expertise for acquiring, assessing, and applying HSPR and priorities in the health system drawing attention away from HSPR. Furthermore, it is perceived by a majority of respondents that the health insurance funds and the physician organisations exert a strong influence in the policymaking process. CONCLUSIONS: Health system and policy researchers in Israel need to be introduced to the benefits and potential advantages of evidence-informed policy in an organised and systematic way. Future research should examine the perceptions of policymakers in Israel and thus we can gain a broader perspective on where the actual issues lie.
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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 | 0.178 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.009 |
| 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, 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".