Peer Support Needs and Preferences for Digital Peer Navigation among Adolescent and Young Adults with Cancer: A Canadian Cross-Sectional Survey
Bibliographic record
Abstract
Adolescents and young adults (AYA) with cancer desire peer support and require support programs that address their unique needs. This study investigated the need for, and barriers to, peer support and preferences for digital peer navigation among AYA. A cross-sectional survey was administered to AYA, diagnosed with cancer between the ages of 15–39, at a cancer center and through social media. Descriptive summary statistics were calculated. Participants (n = 436) were on average 31.2 years (SD = 6.3), 3.3 years since-diagnosis (SD = 3.8), and 65% (n = 218) were women. Over three-quaters (n = 291, 76.6%) desired peer support from cancer peers, but 41.4% (n = 157) had not accessed peer support. Main access barriers were: Inconvenience of in-person support groups (n = 284, 76.1%), finding AYA with whom they could relate (n = 268, 72.4%), and finding AYA-specific support programs (n = 261, 70.4%). Eighty-two percent (n = 310) desired support from a peer navigator through a digital app, and 63% (n = 231) were interested in being a peer navigator. Participants indicated a greater need for emotional (n = 329, 90.1%) and informational support (n = 326, 89.1%) than companionship (n = 284, 78.0%) or practical support (n = 269, 73.6%) from a peer navigator. Foremost peer matching characteristics were cancer-type (n = 329, 88.4%), specific concerns (n = 317, 86.1%), and age-at-diagnosis (n = 316, 86.1%). A digital peer navigation program was desired by over 80% of a large Canadian sample of AYA and could potentially overcome the barriers AYA experience in accessing peer support. The design of a peer navigation program for AYA should consider the matching characteristics and multidimensional support needs of AYA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".