Advancing Health Services Collaborative and Partnership Research Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada – A Call to ‘Re-imagine’ Research"
Bibliographic record
Abstract
Bowen et al highlight the trend towards partnership research to address the complex challenges currently facing healthcare systems and organizations world-wide. They focus on important strategic actors in partner organizations and their experiences, views and advice for sustainable collaboration, within a Canadian context. The authors call for a multi-system change to provide better conditions for research partnerships. They highlight needs to re-imagine research, to move beyond an 'acute care' and clinical focus in research, to re-think research funding, and to improve the academic preparation for research partnerships. In this commentary we provide input to the discussion on practical guidance for those involved in research partnerships based on our partnership experiences from ten research projects conducted within the Swedish healthcare system since 2007. We also highlight areas that need attention in future research in order to learn from approaches used for collaborative and partnership research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.048 | 0.049 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".