A qualitative content analysis of peer mentoring video calls in adolescents with chronic illness
Bibliographic record
Abstract
This article endeavored to determine the topics of discussion during open-ended peer mentoring between adolescents and young adults living with chronic illness. This study occurred alongside a study of the iPeer2Peer Program. Fifty-two calls (7 mentor-mentee pairings) were audio recorded, transcribed verbatim, and analyzed using inductive coding with an additional 30 calls (21 mentor-mentee pairings) coded to ensure representativeness of the data. Three categories emerged: (1) illness impact (e.g., relationships, school/work, self-identity, personal stories), (2) self-management (e.g., treatment adherence, transition to adult care, coping strategies), and (3) non-illness-related adolescent issues (e.g., post-secondary goals, hobbies, social environments). Differences in discussed topics were noted between sexes and by diagnosis. Peer mentors provided informational, appraisal, and emotional support to adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".