“We can do better”: preferred practices and areas for improvement while working with patient advisors in professional organization committees
Bibliographic record
Abstract
Abstract Background: As the best practices for working with patient advisors in organizational committees are still under development, we sought to identify our own experience as the Canadian Association of Psychosocial Oncology—Advocacy Committee. Methods: Committee communications including meeting minutes, email correspondences, and transcript of a webinar delivered by the authors were reviewed to identify the key patient advisor experiences and preferences. Results: We identified practices in good standing and areas to improve. The main areas to improve were related to circumstances or preferences of patient advisors; financial compensation; access and health issues; and the nature of partnerships. The preferred and recommended practices included providing safe spaces; empowering, respecting, and valuing patient advisors; providing resources, guidelines, and remuneration to patient advisors; increasing advisor diversity through more extensive recruitment; and removing the barriers. Conclusions: There is a need for improving patient advisors' experiences for equity and optimum committee work.
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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 | 0.064 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".