Engaging Adolescents With Chronic Illness in Patient‐Education: The Adolescent's Perception
Bibliographic record
Abstract
RATIONALE: Musculoskeletal (MSK) conditions are a leading cause of global disability, yet MSK physical examination remains a well-documented gap in medical education. Learners frequently report low confidence in performing these exams. Medical education programmes have addressed this by engaging adult patient educators with lived experience, an approach that has been shown to improve clinical skills. However, little is known about engaging adolescents as patient educators for the MSK exam, despite arthritis being a common chronic condition in this age group. As adolescents are at a unique developmental stage, their perspectives can help medical learners develop age-appropriate, patient-centred care. Exploring how adolescents themselves perceive this role is essential to designing effective educational programmes. This study represents an important first step in informing the development of a future patient-educator programme involving adolescents with juvenile idiopathic arthritis (JIA). AIMS AND OBJECTIVES: This study explored the perceptions of adolescents (13-18 years) with JIA about their potential involvement as patient-educators of the MSK exam. METHOD: We conducted 19 semi-structured interviews at two Canadian paediatric centres and analysed transcripts using thematic content analysis. RESULTS: Results showed that adolescents were generally enthusiastic about patient education and recognised the value of lived experience in training medical learners. CONCLUSION: Understanding adolescents' perceptions is key to developing future medical education programmes that meaningfully integrate their experiences.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".