Social Networks of Adolescents and Young Adults with Cancer: A Cross-Sectional Study
Bibliographic record
Abstract
A cancer diagnosis disrupts the social networks of adolescents and young adults (AYAs), impacting their overall health and wellbeing. This cross-sectional study examined the social network integration (SNI; size and frequency of contact) of AYAs with cancer in Canada. A survey was distributed to AYAs with cancer at an urban cancer centre and across Canada (n = 334). SNI was measured with the Berkman-Syme Social Network Index (SNI) and a modified version accounting for online interactions (SNI+). A multivariable logistic regression analysis was performed to identify factors associated with SNI and SNI+. A total of 54.8% and 68% of AYAs with cancer were classified as socially integrated with each measure, respectively. Living with others was associated with greater SNI and SNI+ (SNI OR = 3.27, 95% CI = 1.39, 7.72; SNI+ OR = 2.52, 95% CI = 1.14, 5.58), and an annual personal income of >CAD 80,000 was associated with greater SNI+ (SNI+ OR = 2.92, 95% CI = 1.09, 7.77). A significant proportion of AYAs with cancer are socially isolated. AYAs with cancer who live alone and whose personal income is less than CAD 80,000 are at a higher risk of social isolation. Digital technology could be leveraged to increase the SNI of AYAs with cancer.
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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 | 0.001 | 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) | 0.001 | 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".