Experiences and Attitudes Towards Evidence‐Informed Policy‐Making Among Research and Policy Stakeholders in the <scp>C</scp>anadian Agri‐Food Public Health Sector
Bibliographic record
Abstract
Policy-makers working at the interface of agri-food and public health often deal with complex and cross-cutting issues that have broad health impacts and socio-economic implications. They have a responsibility to ensure that policy-making based on these issues is accountable and informed by the best available scientific evidence. We conducted a qualitative descriptive study of agri-food public health policy-makers and research and policy analysts in Ontario, Canada, to understand their perspectives on how the policy-making process is currently informed by scientific evidence and how to facilitate this process. Five focus groups of 3-7 participants and five-one-to-one interviews were held in 2012 with participants from federal and provincial government departments and industry organizations in the agri-food public health sector. We conducted a thematic analysis of the focus group and interview transcripts to identify overarching themes. Participants indicated that the following six key principles are necessary to enable and demonstrate evidence-informed policy-making (EIPM) in this sector: (i) establish and clarify the policy objectives and context; (ii) support policy-making with credible scientific evidence from different sources; (iii) integrate scientific evidence with other diverse policy inputs (e.g. economics, local applicability and stakeholder interests); (iv) ensure that scientific evidence is communicated by research and policy stakeholders in relevant and user-friendly formats; (V) create and foster interdisciplinary relationships and networks across research and policy communities; and (VI) enhance organizational capacity and individual skills for EIPM. Ongoing and planned efforts in these areas, a supportive culture, and additional education and training in both research and policy realms are important to facilitate evidence-informed policy-making in this sector. Future research should explore these findings further in other countries and contexts.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".