Taking Time and Making Space for Patient and Caregiver Partners
Bibliographic record
Abstract
Patient and caregiver engagement is a paramount element to ensure the voice of lived experience is integrated and prioritized in research. However, what it looks like to actually participate in authentic patient and caregiver engagement can be challenging without understanding the experience of our patient and caregiver partners. I had the honour of interviewing a patient and caregiver partner who bestowed rich guidance about what it means to deliver excellent patient and caregiver engagement in research. Themes of the interview included taking time to collaborate, being mindful and giving gratitude, actively listening to partner voices, leading alongside patient and caregiver partners, and making space at the table for all perspectives. Takeaway messages include recognizing the patient and caregiver partners’ value as a whole person, and building genuine relationships. For researchers interested in engaging with patient and caregiver partners, the messages from this interview provide advice to guide future work to encourage sincere collaboration and share the ways in which research can be a meaningful experience for all members of the team.
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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 | 0.023 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".