A Pilot Randomized Control Trial of Teens Taking Charge: A Web-based Self-management Program for Adolescents with Cancer
Bibliographic record
Abstract
Background: There is a lack of self-management tools for adolescents with cancer (AWC). This study evaluated the feasibility of Teens Taking Charge Cancer, a web-based self-management program. Methods: A pilot randomized control trial (RCT) was conducted across 4 pediatric oncology clinics. AWC (12–18 years) and their caregivers were randomized to either the intervention or control group. All were asked to complete 12 website modules over 12 weeks (at their own pace) and received monthly calls from health coaches. The intervention website was based on cognitive behavioral principals, designed as an interactive self-guided online program, while the control consisted of education and included links to 12 general cancer websites. Outcome assessments occurred at enrollment and 12 weeks post-intervention. The primary outcomes included rate of accrual and retention, adherence to the protocol, acceptability and satisfaction with intervention using questionnaire and semi-structured interviews, adverse events and engagement with the intervention. Results: Eighty-one teen-caregiver dyads were enrolled with a retention rate of 33%. In the intervention group 46% ( n = 18) logged in at least once over the 12-week period. A mean of 2.4 of 12 modules ( SD 3.0) were completed; and no one completed the program. Thirty-three percent of caregivers in the intervention logged into the website at least once and none completed the full program. Discussion: The results from this pilot study suggest that the current design of the Teens Taking Charge Cancer RCT lacks feasiblity. Future web-based interventions for this group should include additional features to promote uptake and engagement with the program.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".