Cultivating Value Co-Creation in Health System Research Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada – A Call to Re-imagine Research"
Bibliographic record
Abstract
Partnerships have various purposes and exist in many configurations. Although there has been a refocusing in health system research on forming strategic partnerships between researchers and knowledge users (KUs) to maximise the relevance and uptake of research in practice; research knowledge frequently fails to reach KUs nor impact the community served. Whilst there have been many attempts to engage KUs, researchers and decision-makers often promote a top down approach that has lacked insight into KUs' specific needs and values. Bowen and colleagues uncovered a plethora of negative experiences from a group of Canadian health leaders involved in researcher partnerships. Their comments reflect their experiences seemingly at an earlier stage of a partnership so we were not surprised by their pessimism. However, our experience reflects an established research-health service partnership network where we collaborate and co-create for mutual benefit and with a shared purpose. The reason for its sustained success over several decades is the focus on co-creation of value between stakeholders. Re-imagining must prioritise a paradigm shift towards value co-creation if partnerships are to create opportunities for innovation, productivity and impact.
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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 | 0.014 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.044 | 0.048 |
| Insufficient payload (model declined to judge) | 0.007 | 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".