Integrating Patient-Centred Research in the Canadian Cancer Trials Group
Bibliographic record
Abstract
The inclusion of patients as partners in research is a key link in the delivery of patient-centred care in healthcare systems. Despite genuine intentions to engage patients in authentic partnerships, efforts can result in tokenism and benefits of engagement are missed. Understanding how patient engagement provides value along the research to patient-care continuum and how to best engage patients as partners are key. This document describes the method taken by the Canadian Cancer Trials Group (CCTG) to implement meaningful patient centricity and engagement and the benefits realized. Originally, Patient Representatives were recruited and assigned to CCTG Committees. Lacking guidance, the role was one of a passive meeting attendee. A gap analysis identified a need for clarity in expectations, understanding of the linkage to CCTG strategic objectives, and supporting tools and training. A plan was developed and successfully implemented in three phases, each phase building on the previous, the level of patient engagement simultaneously changing from "Inform" to "Involve" to "Collaborate" on the International Association for Public Participation (IAP2) scale. Results include significant contributions to increased patient accrual in CCTG trials, to increased CCTG grant funding, as well as recognition and adoption of these practices within Canada and internationally.
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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 | 0.496 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".