Empowering partnership: key lessons from the co-development of patient-oriented research with parents, researchers, and healthcare professionals
Bibliographic record
Abstract
Background: Co-developing research in partnership with patients and families is integral to Learning Health Systems (LHSs). These partnerships advance LHS objectives by (1) ensuring innovation is relevant to local contexts, (2) accelerating evidence into practice, and (3) improving services and outcomes that are meaningful to patients and families. Despite the importance of patient and family engagement in LHSs, strategies that guide researchers to build and sustain teams of patients, clinicians, and other partners are under-reported. Objective: We report actionable insights for co-developing research learned through our experience within Alberta's LHS. Context: Parents from a provincial advisory group in Alberta identified the need to evaluate parents' experiences with family-centered care in Neonatal Intensive Care Units (NICUs). In response, a research team of parent partners, researchers, and clinicians is co-developing a validated experience measure for parents in NICUs. Methods: During co-development, the research team engaged in reflective practice through semi-structured discussion informed by Schön's Reflection Model. Notes from the discussion were thematically analyzed to identify insights and research co-development strategies. Results: Three key insights and associated strategies were generated: (1) operationalizing co-development through a shared governance structure, terms of reference, and dedicated reflection; (2) adaptive approaches to team member involvement, renegotiating workflows, and addressing dissent; and (3) team evolution by nurturing reciprocity and utilizing existing partnerships to recruit members. Conclusion: We demonstrate how a team of patient partners, researchers, and clinicians can effectively co-develop research to address health system issues, and we present strategies to support patient-oriented research teams within LHSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".