Policy options to increase motivation for improving evidence-informed health policy-making in Iran
Bibliographic record
Abstract
BACKGROUND: Current incentive programmes are not sufficient to motivate researchers and policy-makers to use research evidence in policy-making. We conducted a mixed-methods design to identify context-based policy options for strengthening motivations among health researchers and policy-makers to support evidence-informed health policy-making (EIHP) in Iran. METHODS: This study was conducted in 2019 in two phases. In the first phase, we conducted a scoping review to extract interventions implemented or proposed to strengthen motivations to support EIHP. Additionally, we employed a comparative case study design for reviewing the performance evaluation (PE) processes in Iran and other selected countries to determine the current individual and organizational incentives to encourage EIHP. In the second phase, we developed two policy briefs and then convened two policy dialogues, with 12 and 8 key informants, respectively, where the briefs were discussed. Data were analysed using manifest content analysis in order to propose contextualized policy options. RESULTS: The policy options identified to motivate health researchers and policy-makers to support EIHP in Iran were: revising the criteria of academic PE; designing appropriate incentive programmes for nonacademic researchers; developing an indicator for the evaluation of research impact on policy-making or health outcomes; revising the current policies of scientific journals; revising existing funding mechanisms; presenting the knowledge translation plan when submitting a research proposal, as a mandatory condition; encouraging and supporting mechanisms for increasing interactions between policy-makers and researchers; and revising some administrative processes (e.g. managers and staff PEs; selection, appointment, and changing managers and reward mechanisms). CONCLUSIONS: The current individual or organizational incentives are mainly focused on publications, rather than encouraging researchers and policy-makers to support EIHP. Relying more on incentives that consider the other impacts of research (e.g. impacts on health system and policy, or health outcomes) is recommended. These incentives may encourage individuals and organizations to be more involved in conducting research evidence, resulting in promoting EIHP. TRIAL REGISTRATION: NA.
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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 | 0.245 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".