A141: Active Engagement of Teens with Juvenile Idiopathic Arthritis in Medical Education: What Do They Think Their Contribution Might Be?
Bibliographic record
Abstract
Background/Purpose: The unique expertise and contributions to teaching of patients with chronic diseases is increasingly recognized and valued in medical education. Specifically in rheumatology, patient educators (also referred as patients partners) programs have been developed and shown to be effective for MSK exam teaching. To our knowledge, this educational strategy has never been adapted to pediatrics. We believe adolescents with juvenile idiopathic arthritis (JIA) might benefit from participation in such educational programs. Since little is known about how teens with JIA could actively be engaged in medical education, the main aim of the study was to explore and develop a detailed understanding of their perspective about potential contributions as patient educators. Methods: Using a qualitative approach, nineteen individual semi‐structured interviews were conducted with adolescents diagnosed with JIA. A purposeful critical case sampling was realized and participants were recruited at two tertiary pediatric hospitals. Consistent with qualitative research analysis, interviews were conducted until theme saturation. A thematic analysis of the transcripts was achieved using a constant comparative approach. In order to facilitate the data analysis, NVivo qualitative research software was utilized. Results: Motivational factors for teens' involvement and main barriers for engagement in patient partners programs were identified. Beyond teaching components of the pediatric musculoskeletal exam, adolescents brought to light other competencies, such as teaching of communication skills, which they valued as important elements to incorporate in the educational program. They also recognized their unique form of knowledge, referred as experiential knowledge, and expressed how it may complement the trainees' biomedical knowledge. Conclusion: Our work demonstrates that adolescents with juvenile idiopathic arthritis express a strong desire to be engaged as patient educators, manifest a clear understanding of what their potential contributions to the teaching might be and vocalize their desire to share with trainees their unique experiential knowledge. This study represents the first step toward the development of patient‐centered arthritis educational programs actively and effectively involving teens with JIA as teachers in medical education.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".