Patient engagement in research: lessons learned from CAPTURE ALS, a longitudinal observational ALS study
Bibliographic record
Abstract
OBJECTIVE: There are compelling ethical and practical reasons for patient engagement in research (PEIR), however, evidence for best practices remains limited. We investigated PEIR as implemented in CAPTURE ALS, a longitudinal observational study, from study inception through the first 2.5 years of operations. METHODS: Data were drawn from three engagement initiatives: a community-led letter-writing campaign; consultation with patient and caregiver focus groups; and a study-embedded 'participant partner advisory council' (PPAC). Data were derived retrospectively from study documentation. We used the International Association of Public Participation (IAP2) participation spectrum as a framework for investigation. RESULTS: 2401 letters from community members to the Canadian government affirmed study objectives and advocated for funding. Feedback from focus group consultation influenced study design and supported the study's data-sharing plan. PPAC collaboration shaped all aspects of the study. Contributions included: co-creation of governance documents, input on study protocols and public-facing communication, and development of engagement webinars for study participants and feedback surveys. Effective communication practices fostered collaboration and helped avoid tokenistic engagement. CAPTURE ALS encompassed all IAP2 participation levels. CONCLUSIONS: CAPTURE ALS was shaped by meaningful engagement initiatives over the course of the study. Lessons learned included: begin early and embed PEIR within research; build relationships and foster mutual learning; be flexible, open to adaptation, and seek diversity. Primary challenges included funding for early implementation, time needed to maintain relationships, and attrition due to disease progression. All IAP2 participation levels contributed to meaningful PEIR. 'Empowerment' was demonstrated through advocacy.
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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 | 0.253 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".