When Health Services Researchers and Policy Makers Interact: Tales from the Tectonic Plates
Bibliographic record
Abstract
There has been a strong push over the last decade for health services researchers to become "relevant," to work with policy makers to translate evidence into action. What has been learned from this interaction? The pooled experiences of health services researchers across the country, including those at the Manitoba Centre for Health Policy (MCHP), suggest five key lessons. First, policy makers pay more attention to research findings if they have invested their own funds and time. Second, researchers must make major investments in building relationships with policy makers, because there are inevitable tensions between what the two parties need and do. Third, researchers must be able to figure out and communicate the real meaning of their results. Fourth, health services researchers need a "back-pocket" mindset, as they cannot count on immediate uptake of results; because the issues never go away, evidence, if known and easily retrievable, is likely to have an eventual impact. Finally, getting evidence into the policy process does not come cheaply or easily, but it can be done. The overriding lesson learned by health services researchers is the importance of relationship-building, whether in formalizing contractual relationships, building and maintaining personal trust, having a communications strategy or increasing the involvement of users in the research process.
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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 | 0.176 | 0.153 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.073 | 0.159 |
| Scholarly communication | 0.087 | 0.082 |
| Open science | 0.009 | 0.068 |
| Research integrity | 0.047 | 0.057 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".