Training curriculum to help patient representatives participate meaningfully in the development of clinical practice guidelines
Bibliographic record
Abstract
Patient participation in the development of clinical practice guidelines (CPGs) is critical for validity and trust. Many guideline panels now include patient representatives. Engagement of these individuals may be improved by training them about the process and their role before they join a guideline panel. To aid patient representatives in engagement in the improvement of guidelines, we developed and implemented a curriculum. The curriculum was developed based on content from the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group and readability principles, and was delivered through a webinar followed by a face-to-face half a day workshop. Twenty-four patient representatives were recruited by the American Society of Hematology to serve on guideline panels. Barriers assessment was facilitated by a pre-curriculum survey. The curriculum targeted patient representatives' knowledge, skills and attitudes and was followed by actual engagement in a guideline panel and a post-curriculum survey. Participants reported that the combination of the two training methods was very useful (9/10 on the Likert scale) in increasing their knowledge about guideline development. They agreed that their skills and self-efficacy in developing guidelines improved (8/10). Their attitudes (confidence in their ability to participate) improved by 30% between the webinar and the workshop. They developed a script to use during panel deliberations and an instruction sheet for the guideline panel about how to empower and engage them as active participants in the guideline development process. The benefits of incorporating patients' voice in CPGs are multifold. These benefits may be optimised by providing patient representatives with training that addresses barriers to engagement and tools to increase their knowledge, skills and attitudes required for meaningful participation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | high |
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".