Engaging Cancer Patients in Clinical Practice Guideline Development: A Pilot Study
Bibliographic record
Abstract
Background: Patient engagement is a key quality component of cancer guideline development; however, the optimal strategy for engaging patients in guideline development remains unclear. The feasibility and efficacy of two patient engagement models was tested by Cancer Care Ontario's cancer guideline development program, the Program in Evidence-Based Care (pebc). Methods: In model 1, patients participated in the guideline development process as active members of a working group. In model 2, patients formed a separate consultation group to review project plans and recommendations generated by multiple working groups. Training included online resources (model 1) and an in-person orientation (model 2). The pebc's standard patient engagement process acted as a control. The study was conducted for 1 year. Surveys measured the satisfaction of patients and members of the guideline working groups with the process and the outcome of each model. Results: Three guideline projects used model 1 to engage patients, six projects used model 2 to receive feedback, and one project was used as a control group (14 patients total). Most participants, whatever the model, reported satisfaction with their experience. Key challenges to implementation included patient recruitment and long wait times between meetings (model 1), and difficulty focusing on the discussion topic and poor meeting attendance on the part of patients (model 2). Conclusions: The pilot study demonstrated that, although both models are feasible and effective for the engagement of patients in cancer guideline development, modifications are required to optimize their continued interest. The pebc will use the study results to inform the implementation of a patient engagement strategy for its program.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".