Co-learning commentary: a patient partner perspective in mental health care research
Bibliographic record
Abstract
BACKGROUND: Although including patients as full, active members of research teams is becoming more common, there are few accounts about how to do so successfully, and almost none of these are written by patient partners themselves. Three patient partners contributed their lived experience to a three-year, multi-component mental health research project in British Columbia, Canada. As patient partners, we contributed to innovative co-learning in this project, resulting in mutual respect and wide-ranging benefits. To guide future patient partners and researchers seeking patient engagement, we outline the processes that helped our research team 'get it right'. MAIN BODY: From the outset, we were integrated into components of the project that we chose: thematically coding a rapid review, developing questions and engagement processes for focus groups, and shaping an economic model. Our level of engagement in each component was determined by us. Additionally, we catalyzed the use of surveys to evaluate our engagement and the perceptions of patient engagement from the wider team. At our request, we had a standing place on each monthly meeting agenda. Importantly, we broke new ground when we moved the team from using previously accepted psychiatric terminology that no longer fit the reality of patients' experiences. We worked diligently with the team to represent the reality that was appropriate for all parties. The approach taken in this project led to meaningful and successfully integrated patient experiences, fostered a shared understanding, which positively impacted team development and cohesion. The resulting 'lessons learned' included engaging early, often, and with respect; carving out and creating a safe place, free from stigma; building trust within the research team; drawing on lived experience; co-creating acceptable terminology; and cultivating inclusivity throughout the entire study. CONCLUSION: We believe that lived experience can and should go hand-in-hand with research, to ensure study outcomes reflect the knowledge of patients themselves. We were willing to share the truth of our lived experience. We were treated as co-researchers. Successful engagement came from the 'lessons learned' that can be used by other teams who wish to engage patient partners in health research.
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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 | 0.131 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.047 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.011 | 0.028 |
| Research integrity | 0.043 | 0.076 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".