The Experiences of Adolescents and Young Adults with Digital Supportive Care Interventions for Cancer: A Systematic Review of Qualitative Studies
Bibliographic record
Abstract
Background: Evidence suggests the importance of cancer supportive care for adolescents and young adults (AYAs), and digital technology may provide tailored care that is flexible, affordable and accessible. However, AYAs’ experiences with these digital cancer supportive care interventions are currently unclear. Objective: The aim of this review is to systematically identify and explore potential intervention facilitators, barriers and areas of improvement. Methods: We conducted a comprehensive search of MEDLINE (Ovid), EMBASE, PsycINFO and CINAHL for mixed methods and qualitative studies, published between 2000 and 2023, focusing on the experiences of AYAs between the ages of 15 and 39 years using digital supportive care interventions for cancer. Studies involving only pediatric and older populations were excluded. The identified studies were critically appraised and thematically analyzed. Results: Twenty-three digital interventions were identified. They varied in modality and addressed different aspects of supportive care (e.g., physical activity, psychological well-being and symptom management). Participants’ experiences with the intervention attributes (e.g., appropriate content, flexible choices, seamless technology and inclusive environment) influenced their physical and psychological health, connections and communication skills, and autonomy. Conclusions: Overall, AYAs reported favorable experiences with digital interventions when provided with tailored supportive care for cancer. Digital interventions may help to increase reach and access to supportive care for cancer; however, barriers to delivery, such as faulty technology or cumbersome intervention features, can negatively impact participant experiences and may reduce engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".