The iPeer2Peer mentorship program for adolescent thoracic transplant recipients: An implementation-effectiveness evaluation
Bibliographic record
Abstract
BACKGROUND: An increase in self-management skills for adolescent thoracic transplant recipients may improve health outcomes and facilitate a successful transition to adulthood. The iPeer2Peer program is an online peer mentorship program that has been successfully implemented as a self-management intervention in multiple chronic disease populations. This study aimed to determine the implementation and effectiveness outcomes of the iPeer2Peer program for adolescent thoracic transplant recipients. METHODS: A type III, hybrid implementation-effectiveness pilot study that comprised a quasi-experimental single-arm pre-post design was used to evaluate the iPeer2Peer program. Participant mentees, ages 12-17, were recruited from 2 large Canadian transplant centers. Peer mentors, ages 18-25, were thoracic transplant recipients who had successfully transitioned to adult care and self-manage their condition. A mixed methods approach for data collection was used, including interviews, focus groups, and standardized questionnaires. RESULTS: Twenty mentees (median 15.0 years, IQR 3.3 years; 65% female) completed the iPeer2Peer program with 9 young adult mentors (median 21.0 years, IQR 3.0 years; 78% female). Implementation outcomes indicated that the iPeer2Peer program was perceived as feasible, adoptable, acceptable, and appropriate for adolescent thoracic transplant recipients. Significant findings were noted in mentees for increased self-management and a decrease in overall depression and anxiety symptoms. CONCLUSIONS: The successful implementation of the pilot iPeer2Peer program offers support to evaluate the scalability, sustainability, and cost-effectiveness of the program for adolescents with chronic illness, specifically thoracic transplant recipients. Changes to the iPeer2Peer program that facilitate a flexible delivery may help implementation and acceptance.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".