Adapting patient and public involvement in patient‐oriented methods research: Reflections in a Canadian setting during COVID‐19
Bibliographic record
Abstract
BACKGROUND: Processes of the patient and public involvement (PPI) in health research shifted quickly during 2020. Faced with large-scale issues, such as the COVID-19 pandemic, the need to adapt processes of PPI to uphold commitments to nurturing the practice of 'nothing about us without us' in research has been urgent and profound. We describe how processes of PPI in research on patient-oriented methods of knowledge translation and implementation science were adapted by four teams in a Canadian setting. METHODS: As part of an ongoing quality improvement self-study to enhance PPI within these teams, team members shared their experiences of PPI in the context of this pivotal year during interviews and facilitated discussions. Drawing on these experiences, we outline challenges and reflections for adapting processes of PPI in health research on methods in times of urgency, conflict and fast-moving change. DISCUSSION: Our reflections offer insight into common issues encountered across teams that may be amplified during times of rapid change, including handling change and uncertainty, sustaining relationship-building and hearing differing perspectives in processes of PPI. CONCLUSION: These learnings present an opportunity to help others active in or planning patient-oriented methods research to reflect on the changing nature of PPI and how to adapt PPI processes in response to turbulent situations in the future.
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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 | 0.118 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.071 | 0.049 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".