Identifying Adaptations to an mHealth Alcohol Reduction Intervention for Reducing Alcohol Use in Adolescent and Young Adult Cancer Survivors: Qualitative Study
Bibliographic record
Abstract
Background: Adolescent and young adult survivors of cancer (aged 15-39 years) frequently engage in hazardous alcohol use, which can have multiple mental and physical health effects. Objective: The aim of this study was 2-fold, to identify the necessary adaptations to an existing motivational interviewing-based mHealth (mobile health) alcohol reduction intervention, called Tracking and Reducing Alcohol Consumption (TRAC), for posttreatment adolescents and young adults, and to develop a tailored intervention for this vulnerable and underserved population. Methods: This was a qualitative study consisting of key informant interviews with posttreatment adolescents and young adults aged 18-39 years, oncology and psych-oncology providers, and community advocates (n=15) to inform the adaptation of TRAC. Thematic qualitative analysis of interview findings was conducted to determine necessary changes to the intervention protocol and content, which would ultimately lead to the development of the new TRAC adolescent and young adult intervention. Results: Key informant interviews revealed a need for the intervention to address cancer-specific alcohol use triggers such as scan-related anxiety, financial toxicity, and reproductive health concerns. They also indicated the need to provide education on the link between alcohol and cancer and to reduce the time burden of the intervention, given the many competing life demands of adolescents and young adults. Significant changes were made to the TRAC intervention to create the TRAC adolescent and young adult. We reduced the number of required sessions from 8 to 4, introduced a session devoted to managing cancer-specific triggers, and provided survivors with more information about alcohol and its relationship to cancer. Conclusions: There is potential to increase alcohol intervention relevance and fit for adolescents and young adults by including tailored content relevant to their life experiences while also maintaining core components of such interventions, such as self-monitoring and goal-setting. Remote, brief interventions are important for ensuring acceptability. The new TRAC adolescent and young adult intervention represents a potentially valuable tool in addressing high rates of hazardous alcohol use among this population and warrants further evaluation in randomized trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".