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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".