Palliative and Hospice Care Mobile Applications: A Comprehensive Review and Recommendations
Bibliographic record
Abstract
Palliative and hospice care, traditionally provided in hospitals, are expanding into homes and communities with an increase in the elderly population and number of patients with chronic diseases. Despite the development of various mobile applications, the characteristics and relevance of these technologies in community settings have been insufficiently analyzed, resulting in a lack of clarity regarding their effectiveness. This systematic scoping review aimed to analyze and categorize the characteristic features of mobile applications for palliative and hospice care, and propose key attributes that should be included in community setting based applications. Sixty-six mobile applications for palliative and hospice care were included through mobile platforms (Google Play and the iOS Appstore). Of the total number of palliative and hospice care applications, 44 were intended for use by clinicians and 14 by patients and informal caregivers, respectively The application features can be categorized into eight themes: 1) information and procedures for palliative care and hospice facilities, 2) visiting management, 3) medical record management and documentation, 4) palliative and hospice care guidance and education, 5) care planning management, 6) communication and notification systems, 7) administrative services for facilities, and 8) data management. Determining whether the current applications are clinically effective is challenging. Future palliative care˚uhospice applications should integrate existing disparate functions to provide users with a one-stop solution for palliative and hospice care-related services and allow for care beyond the in-hospital setting, thus providing patients with more end-of-life care options.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".