Bridging the gap between science and policy: an international survey of scientists and policy makers in China and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Bridging the gap between science and policy is an important task in evidence-informed policy making. The objective of this study is to prioritize ways to bridge the gap. METHODS: The study was based on an online survey of high-ranking scientists and policy makers who have a senior position in universities and governments in the health sector in China and Canada. The sampling frame comprised of universities with schools of public health and medicine and various levels of government in health and public health. Participants included university presidents and professors, and government deputy ministers, directors general and directors working in the health field. Fourteen strategies were presented to the participants for ranking as current ways and ideal ways in the future to bridge the gap between science and policy. RESULTS: Over a 3-month survey period, there were 121 participants in China and 86 in Canada with response rates of 30.0 and 15.9 %, respectively. The top strategies selected by respondents included focus on policy (conducting research that focuses on policy questions), science-policy forums, and policy briefs, both as current ways and ideal ways to bridge the gap between science and policy. Conferences were considered a priority strategy as a current way, but not an ideal way in the future. Canadian participants were more in favor of using information technology (web-based portals and email updates) than their Chinese counterparts. Among Canadian participants, two strategies that were ranked low as current ways (collaboration in study design and collaboration in analysis) became a priority as ideal ways. This could signal a change in thinking in shifting the focus from the "back end" or "downstream" (knowledge dissemination) of the knowledge transfer process to the "front end" or "upstream" (knowledge generation). CONCLUSIONS: Our international study has confirmed a number of previously reported priority strategies to bridge the gap between science and policy. More importantly, our study has contributed to the future work on evidence-based policy making by comparing the responses from China and Canada and the current and ideal way for the future. Our study shows that the concept and strategies of bridging the gap between science and policy are not static but varying in space and evolving over time.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it