Connectedness to the young adult cancer community and post‐traumatic growth: A young adults with cancer in their prime study
Bibliographic record
Abstract
OBJECTIVE: For young adults (YAs) with cancer, connecting with peer cancer survivors can provide a unique sense of community and may enhance post-traumatic growth (PTG). This study examined the relationship between connectedness to the YA cancer community and PTG among YAs, independent of overall social support. METHODS: Data were obtained from the young adults with cancer in their prime study, a cross-Canada survey of YA cancer survivors. Participants were stratified by level of social support into two groups (low/high). Multivariable logistic regression was used to examine the association between PTG and connectedness to the YA community adjusting for respondent characteristics, and the interaction between support and connectedness. RESULTS: Of 444 respondents, mean age was 34.2 (SD = 6.0), time-since-diagnosis was 4.8 years (SD = 5.4), and 87% were female. Over two-thirds of respondents (71%) reported feeling connected to the YA community. Level of connectedness to the YA community did not differ by social support group, and interaction between social support and connectedness to the YA community was not significant. In the adjusted regression, connectedness to the YA community (aOR = 2.29, 95% CI: 1.10-4.91), high social support (aOR = 2.98, 95% CI: 1.36-6.74), greater time-since-diagnosis (aOR = 1.09, 95% CI: 1.04-1.15) and female sex (aOR = 2.21, 95% CI: 1.23-4.04) were associated with greater odds of moderate-to-high PTG. CONCLUSIONS: Feeling connected to a community of YA cancer peers was associated with moderate-to-high PTG among YAs, independent of overall perceived social support. Future efforts should increase access to YA cancer communities and foster a sense of connectedness among YAs 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".