Smartphone apps to help children and adolescents with cancer and their families: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it