Smartphone apps to help children and adolescents with cancer and their families: a scoping review
Bibliographic record
Abstract
Introduction: Considering the importance of empowering patients and their families by providing appropriate information and education, it seems smartphone apps provide a good opportunity for this group. The purpose of this review was to identify studies which used smartphone apps to help children and adolescents with cancer and their families.Method: Arksey and O’Malley’s framework was employed in this review. To examine the evidence on the design and use of smartphone apps for the target group, PubMed, Embase, Scopus and Web of Science databases were searched from 2007 to November 2018.Results: Twenty-four articles met the inclusion criteria, with 33% being conducted in the USA and 21% in Canada. Moreover, in 20 studies (83%), app was specifically designed for children and adolescents, with only three studies (13%) for parents and one study (4%) for both. The main modules of smartphone apps in these studies included symptom assessment (90%), provision of information and education (74%), communication with caregivers (57%), social support (30%) and calendar and reminder (21%).Conclusions: Due to the easy access to smartphones without a costly infrastructure compared to landline phones, the use of mobile health (m-Health) has become a suitable method of providing healthcare services, especially for cancer. Use of smartphone apps, increases patient and families’ access to reliable and suitable education and information regarding the disease. Thus, healthcare policy-makers in developing or underdeveloped countries can exploit the health-related potentials of m-Health following the experience of developed countries.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".