Insights From Diverse Perspectives on Social Media Messages to Inform Young Adults With Cancer About Clinical Trials: Focus Group Study
Bibliographic record
Abstract
Background: Low rates of adolescent and young adult (YA; aged 15-39 y) clinical trial enrollment (CTE), particularly among underserved groups, have resulted in a lack of standardized cancer treatments and follow-up guidelines for this group that may limit improvement in cancer treatments and survival outcomes for YAs. Objective: To understand and address unique barriers to CTE, we conducted focus groups to learn about informational, financial, and psychosocial needs of YAs surrounding CTE and identify strategies to address these barriers. Methods: We conducted 5 focus groups in 2023 among a diverse sample of YA patients from across the United States. An interview guide was developed collaboratively with YA advocates. Specifically, informational needs, financial concerns, and psychosocial issues were explored, and participants were probed to suggest strategies, especially those that leverage technology, to address these barriers. Sessions were audio recorded, transcribed, and coded using direct content analysis. Findings were synthesized through consensus discussions. Results: We confirmed the previously proposed thematic barriers regarding YA CTE and identified 9 subthemes: awareness, lack of clear and accessible CTE information, fear of the unknown, assumptions about costs, insurance coverage, navigating financial responsibilities, clinical trial discussions, clinical trial misconceptions, and desire for a support network. Throughout, YAs mentioned needs that might be addressed through informational outreach leveraging digital technology, the internet, and social media. Conclusions: This study expands knowledge of YA perceived barriers to CTE. These findings suggest that leveraging digital technology to disseminate reliable information to address needs may be an effective strategy to improve clinical trial participation in the YA population.
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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 | 0.027 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".