The colliding cultures of healthcare systems and academic research: the role of research teams
Bibliographic record
Abstract
Background: The imperative to conduct relevant and responsive research to improve healthcare for patients, families, and communities, means researchers must develop best practices to work in concert with the health system research needs. Audience: This workshop will interest researchers, patient partners, and policy makers who work alongside each other for health system transformation. The session will explore challenges that arise during health system research and potential solutions to these challenges. Participants are encouraged to bring their own examples of what has worked well, what has not, and why (or why not). Approach:Introduction (0 minutes)Designed to foster discussion and generate solutions to challenges commonly faced by research teams when engaging in health system research, this session will encourage researchers, patient partners, and policy makers to share their own research experiences. This workshop, co-led by patient partners and researchers from Saskatchewan, Canada, will outline the provincial context for health services research. This will include a description of the relationships among patient partners, researchers, and Saskatchewan Health Authority (SHA) policy makers. Case Studies (5 minutes)The case studies will be briefly introduced by the principal investigators and patient partners, who will provide an outline of how, why, and for whom, what worked, and/or what did not work. These patient-oriented research (POR) case studies demonstrate a spectrum of successful engagement with health system transformation. The first case study addresses the multiple challenges presented when the researcher had limited access to control over the resources. The second case study highlights the opportunities for the research to be more responsive to changes in the direction of the project when the researcher was an embedded member of a health system working group. The final case study illustrates a situation where the researcher was an embedded co-lead of the health system initiative. Each case will also include a description of the challenges patient partners faced when collaborating across the two complex systems. After presenting the lessons learned from the case studies, participants will be provided with questions to engage them in small group discussions of their own experiences with the challenges and successes of POR health system research. Small Group Discussion (25 minutes)Participants will be invited to discuss their perspectives regarding POR in health systems. Each group of 4-6 participants will be facilitated by a patient partner and a designated group member will record and report on the group discussion. For the first ten minutes, participants will be asked to discuss: What are the similarities and differences between your own experiences and the case studies that were presented? Next, participants will be asked: what are your perspectives on best practices to overcome challenges engaging in research with multi-stakeholder teams of patient partners, researchers, and policy-makers? By the end of the discussion, each group will have identified one take-home message about best practices for health system transformation research. Group Feedback and Reporting (0 minutes)The designated group member will report their conclusions to the larger group. The goal will be to identify commonalities across the groups. Key commonalities will be recorded by workshop co-leads on PowerPoint slides. Summary and Closing (5 minutes)The key takeaways from the session will be summarized by the workshop facilitator, who is an expert in knowledge mobilization. The content of the slides will be disseminated to interested workshop participants following the session for future reference.
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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 | 0.427 | 0.327 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.058 | 0.155 |
| Scholarly communication | 0.097 | 0.076 |
| Open science | 0.010 | 0.085 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".