How to Work Collaboratively Within the Health System: Workshop Summary and Facilitator Reflection
Bibliographic record
Abstract
Effectiveness in health services research requires development of specific knowledge and skills for working in partnership with health system decision-makers. In an initial effort to frame capacity-building activities for researchers, we designed a workshop on working collaboratively within the health system. The workshop, based on recent research exploring health system experience and perspectives on research collaborations, was trialed at the annual Canadian Health Services and Policy Research (CAHSPR) conference in May 2019. Participants reported positive evaluations of the workshop. However, further efforts should target health services researchers that may not be as motivated to develop skills in collaborative research. Additional attention to equipping researchers with the skills needed to work in partnerships is recommended, including approaches and materials that avoid oversimplification of complex challenges.
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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 | 0.088 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.009 | 0.018 |
| 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".