Understanding optimal approaches to patient and caregiver engagement in the development of cancer practice guidelines: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Practice guidelines (PGs) can assist health care practitioners and patients to make decisions about health care options. A key component of high quality PGs is the consideration of patient values and preferences. A mixed methods study was conducted to understand optimal approaches to patient engagement in the development of cancer PGs. METHODS: Cancer patients, survivors, family members and caregivers were recruited from cancer clinics, follow-up clinics, community support programs, a provincial patient and family advisory committee, and a provincial cancer PG development program. Participants attended a workshop, completed a survey, or participated in a telephone interview, to provide information about PG awareness, attitudes, information needs, training, engagement approaches and barriers and facilitators. RESULTS: Forty-one participants (12 workshop attendees, 21 survey respondents and 8 interviewees) provided data. For those with no PG development experience, fewer than half were previously aware of PGs but perceived several benefits to the inclusion of this perspective. Common barriers to participation across the groups were time commitment, duration of the PG development process, and financial costs. Positive beliefs about the contributions that could be made and practical considerations (e.g., orientation and training, defined roles and expectations) were identified as key features in the successful integration of patients into the PG development process. There was no single model of engagement favored over another. CONCLUSIONS: Study results align with similar studies in other contexts and with international patient engagement efforts. Findings are being used to test new patient engagement models in a programmatic PG development initiative in Ontario, Canada.
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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 | 0.084 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".