A comparison of the sociodemographic, medical, and psychosocial characteristics of adolescents and young adults diagnosed with cancer recruited in-person and online: A Canadian cross-sectional survey
Bibliographic record
Abstract
Introduction Adolescents and young adults diagnosed with cancer (AYAs) are under-represented in research. The Internet and social media could increase the reach of recruitment efforts but may impact sample characteristics. This study evaluated the characteristics of AYAs recruited in-person at an urban hospital versus the Internet in terms of their sociodemographic and medical characteristics, and psychosocial wellbeing, and offers recommendation for increasing the inclusivity and representativeness of research samples. Methods Participant data from a cross-sectional survey of AYAs in Canada were evaluated. In-person hospital recruitment used a registry to identify patients attending ambulatory clinics. Internet recruitment included notices on hospital, team members’, and community partners’ social media channels, and email newsletters. Independent sample t -tests and Chi-squared tests were used to identify differences in participant sociodemographic, medical, and psychosocial characteristics based on recruitment source. Results Of 436 participants, 217 (49.8%) were recruited in-person and 219 (50.2%) online. Online participants were more likely: to be white ( p < .001), women ( p < .001), and Canadian-born ( p < .001); to speak English at home ( p < .001), live alone ( p = .001) and live in rural settings ( p = .014); and to be farther from diagnosis ( p = .023), diagnosed with breast cancer ( p < .001), and cancer free ( p < .001) compared to the hospital sample. Online participants also reported higher anxiety, depression, and loneliness ( p < .001), and lower social support (p < .001), self-efficacy for coping with cancer ( p < .001), and life satisfaction ( p = .006). Conclusions Online recruitment yielded a more geographically diverse but less sociodemographically diverse sample of AYAs who were farther from diagnosis and had poorer psychosocial wellbeing than in-person recruitment at an urban hospital. Future research efforts should consider partnering with under-represented communities and using targeted and stratified online and in-person recruitment strategies to achieve an inclusive and representative sample of AYAs.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".