Learning to work together: experiences of researchers and family partners in a patient-oriented research team
Bibliographic record
Abstract
The BetterLTC research team holds ten years of experience conducting patient-oriented research. We are an intergenerational multicultural patient-oriented research team. Our team comprises researchers, clinicians, trainees, and family partners (experts by lived experience). In this commentary, we come together to share our experiences as researchers and family partners: what we think about and how we live patient-oriented research. We recorded and explored our experiences from the ground up. We came together to thematically analyze our recordings, and we encountered themes within our conversations. From our shared experiences, we make visible the common themes of balancing diverse expectations, raising the voice of diverse experiences, and valuing relationship building. As a team, we have encountered challenges within the academic culture of high productivity expectations. We navigate challenges by acknowledging the diverse expectations within the team and valuing each person`s perspectives and our time together for relationship building. We learned that patient-oriented research is more than following policies and guidelines for engaging family partners; it is a way of being in the world that cultivates curiosity and openness to surprise. Through working together, we create spaces for listening, learning, and developing meaningful research. We are a patient-oriented research team, and during our time together, we have learned the importance of cultivating openness to diverse perspectives and building ethical relationships with each other. In this commentary, we share our experiences as an intergenerational patient-oriented research team. We are a team of researchers, clinicians, trainees, and family partners (experts by lived experience). Patient-oriented research is not free of challenges, as the academic culture often pushes for high productivity, not supporting the time needed to engage with all team members. Our team recognizes the value of conducting patient-oriented research and acknowledges the positive impact it can have on healthcare systems.
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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 | 0.030 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.012 |
| 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".