Strategies to improve adherence to treatment in adolescents and young adults with cancer: a systematic review
Bibliographic record
Abstract
Purpose: Adolescents and young adults (AYAs) with cancer have higher rates of nonadherence to treatment relative to younger and older cancer patients. Efforts to improve adherence in this population are therefore increasing. This review aimed: 1) to synthesize recommendations and strategies used to improve treatment adherence in AYAs with cancer, and 2) to summarize the available evidence supporting the efficacy of adherence-promoting strategies for AYAs with cancer. Methods: We conducted a systematic review with two stages: 1) a narrative stage, to analyze expert recommendations, and 2) an evaluative stage, to summarize quantitative evidence for interventions. Four electronic databases were searched for studies involving AYAs, aged 10–39 years, with cancer, published from 2005 to 2015. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to ensure quality of the review. The Delphi list was used to assess study quality. Results: Nine articles were identified in the narrative stage of the review. For the evaluative stage, out of 113 screened abstracts, only one eligible intervention was identified. Common themes of adherence-promoting strategies were grouped into five domains: developmental, communication, educational, psychological well-being, and logistical/management strategies. Strategies to address developmental stage and to improve communication were the most highly recommended to improve adherence. Few strategies focused on the role of the patient in adherence. One intervention found that a behaviorally targeted computer game could significantly improve adherence to prescribed oral medication in AYAs with cancer. Conclusion: Although numerous studies report challenges to treatment adherence in AYAs with cancer, little research has systematically evaluated the impact of implementing recommended strategies and interventions in this age group. The present review extends the current literature through its focus on strategies recommended to improve adherence, rather than focusing on barriers and risk factors for nonadherence. There is now a need for more rigorous research to systematically assess the effect of implementing strategies to improve AYAs' adherence to cancer treatment. Keywords: neoplasms, emerging adulthood, interventions, communication, psychosocial
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".