Mobile phone apps for family caregivers: A scoping review and qualitative content analysis
Bibliographic record
Abstract
BACKGROUND: The growth of mHealth apps has been exponential in recent years, but there is limited knowledge regarding the availability, functionality, and quality of apps to support family caregivers. Our objectives were to identify the apps currently available to support family caregivers and to analyze the app functions and evaluation claims. METHODS: This scoping review was conducted across the iOS, Android, and Windows Phone app stores in three steps: (1) electronic app search; (2) iterative inclusion and exclusion criteria development; (3) mixed-method analysis of app characteristics and evaluation claims. RESULTS: The search identified 1008 apps; 175 met our inclusion/exclusion criteria. Most apps offered either one (36%, 63/175) or two (41%, 71/175) specific functions, the most common of which were access to service and provider directories, providing patient-caring tips, and tools to facilitate daily activities associated with caring for a loved one. For fully two-thirds (67%, 118/175) of the identified apps, the functions serve to assist caregivers to support the care recipient as opposed to supporting the family caregivers themselves. CONCLUSIONS: The findings of this review indicate that, while a wide range of family caregiver apps are now available across the mHealth landscape, most apps offer limited functionality. Therefore, there is a need for multi-functionality to avoid the inherent challenges that caregivers may experience when navigating and managing multiple apps to meet all their various needs. Moreover, as this specific niche continues to develop, greater attention should be devoted to supporting family caregivers' own personal care needs as caregiver burden is a pressing challenge.
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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 | 0.065 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".