How Can Primary Care Researchers Make Sure Their Findings Speak to the World of Health Policy Makers?
Bibliographic record
Abstract
Context: Questions regarding the most effective ways to translate knowledge and evidence into primary care have been ranked among the top ten international priorities for primary care research. Objective: To share learnings about engaging with policy-makers to promote the use of PHC research. Study Design and Analysis: The Canadian Transdisciplinary Understanding and Training on Research-Primary Heath Care (TUTOR-PHC) is a unique program that develops capacity for interdisciplinary PHC research. Part of the TUTOR-PHC curriculum includes a workshop for trainees to gain skills and knowledge in engaging with policy-makers for the purposes of knowledge mobilization. A thematic analysis of the summaries from the workshops over the last 4 years was conducted using the SPIRIT Action Framework to frame the themes. The SPIRIT framework describes a point in research uptake where policy-makers are receptive to research – a “catalyst stage”, and notes that the response to this catalyst is dependent on the capacity of policy-makers to use the research. Setting or Dataset: Canada Population Studied: N/A Intervention/Instrument: N/A Outcome Measures: N/A Results: Themes included practical actions researchers can take to influence the catalyst stage and policy maker-capacity as follows: Catalyst: adopt the mindset of creating knowledge and also creating change based on the evidence we produce; strive to understand policy-maker’s context, describe the alignment between your research and the policy-maker’s reality; develop ongoing relationships with policy-makers to facilitate the uptake of evidence and co-creation of questions; and understand that policy-makers bring experiential knowledge. Capacity: Anticipate the policy-maker’s knowledge needs and provide solutions; develop trusting relationships; invite the policy-maker to be part of the research; use your own knowledge to place research findings in context; bring evidence from other jurisdictions to enhance the knowledge of the policy-maker; and present information on what is feasible, actionable, alternatives and costs. Conclusions: PHC researchers can enhance their engagement with policy-makers through understanding context, relationship building and skillful synthesis of existing evidence. TUTOR-PHC recognizes this essential component of PHC research and is committed to capacity building in this area.
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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 | 0.681 | 0.829 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.020 | 0.059 |
| Scholarly communication | 0.070 | 0.070 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.039 | 0.040 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier 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".