Engaging public health decision makers in partnership research
Bibliographic record
Abstract
Involving decision makers in collaborative research partnerships can help increase the relevance and timeliness of the research question, and ensure the results are more readily applied in practice. These partnerships offer great benefits but also unique challenges. We will discuss some of these challenges, as identified from our recent study (Canadian Institutes of Health Research FRN 101867, 126353) and the growing literature on engaging decision-makers in knowledge translation (KT) research. We will also recommend strategies for ensuring a successful partnership. We collaborated with three Canadian public health departments to enhance capacity for and facilitate organizational contexts conducive to evidence-informed decision making (EIDM). The research team and decision-maker partners jointly developed the research questions and KT strategies, tailored to each partner's organizational needs and goals. Intervention effectiveness was assessed via quantitative (online survey, in-person assessment) and qualitative (interviews, reflective journal entries, case study notes) data; this discussion has been informed, in part, by the qualitative analysis. Identified challenges include: unpredictable practice settings and a change in priorities over time; time and staff workload; decision maker research knowledge and prior experience; and balancing applied research with rigorous scientific practice. To mitigate these challenges, we recommend: sustaining open and ongoing communication to maintain momentum and reinforce commitment; identifying a key contact to help facilitate and promote the study; obtaining formal approval on timelines, goals, communication, and role expectations; and establishing a mutual understanding of the research and decision-making processes. A strong relationship between researchers and decision makers, built on mutual respect, trust, and understanding, is critical for successful partnerships. This paradigm is continuing to gain recognition as an effective approach to KT research. Our discussion on possible challenges (and their associated solutions) will help ensure both researchers and decision makers are able to enter more productive partnerships.
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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 | 0.312 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.034 | 0.026 |
| Open science | 0.007 | 0.059 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".