Developing a patient-educator program for adolescents with juvenile arthritis: exploring motivation sources, barriers, and facilitators
Bibliographic record
Abstract
Background: Patients can be valuable contributors to medical education, offering nuanced perspective and guidance, based on lived experience. While numerous training programs have integrated adult patients in this manner, very few have engaged adolescent patients. Recognizing adolescent educators may be particularly helpful in teaching pediatric conditions, this study appraises the potential for including those with juvenile arthritis in pediatric rheumatology training. Methods: Using an exploratory qualitative approach, we conducted semi-structured interviews with adolescents with juvenile arthritis receiving treatment at two tertiary-care pediatric centres in Canada about the motivations, perceptions, facilitators, and barriers that influence their engagement as patient educators. The interview transcripts were analyzed using an iterative qualitative descriptive method. Results: = 19, aged 13-18) identified intrinsic factors, such as learning about their condition and socializing with peers, and extrinsic factors, such as helping students learn and promoting greater disease awareness, as relevant drivers for participating as patient educators. They pointed to balancing school and medical appointments, transportation, and discomfort with sharing personal experiences in large groups as barriers. Parental support, accruing volunteer hours, and engaging health professionals in the teaching sessions were seen as facilitators that could meaningfully enhance the relevance and impact of adolescents' contributions to medical education. Conclusion: Adolescents with juvenile arthritis are motivated to participate as patient educators. Understanding the factors that promote their involvement supports the development of training initiatives involving adolescents that are likely to be successful and sustainable.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".