Integrating Telehealth into Community-Based Palliative Care: A Systematic Review
Bibliographic record
Abstract
Introduction: Community-based palliative care implementation faces challenges in terms of providing services to people living with terminal illness in remote and underserved areas. Integrating telehealth into palliative care has the potential to improve access to and effectiveness of palliative care by enabling remote interactions between people living with terminal illness, their caregivers, and healthcare providers. However, the models and outcomes of telehealth in the context of community palliative care remain poorly understood. Objective: The aim of this systematic review was to investigate the benefits of integrating telehealth into community-based palliative care for people living with terminal illness and their caregivers. Methods: A systematic search of studies was conducted using MEDLINE, PubMed, EBSCO, Cochrane Controlled Register of Trials (CENTRAL), Scopus, and Google Scholar. The inclusion criteria were primary quantitative studies on integrating telehealth into palliative care in community for adults living with terminal illness, published in between 2014 and 2024. The risk of bias was assessed using the revised Cochrane risk of bias tool for randomized controlled trials and the Newcastle-Ottawa scale for cohort studies. The data were analyzed using content analysis. Results: Seven studies met the inclusion criteria. Telehealth interventions most commonly involve telephone or video consultations and phone calls. Evidence has shown consistent improvements in functional status, reduction in hospitalization rate, and reductions in psychological distress (anxiety, depression) among people living with terminal illness. For caregivers, the benefits included reduced psychological distress (stress, depressive symptoms) and care burden. Additionally, improvements in quality of life among caregivers has been inconsistent. Conclusion: Integrating telehealth into community-based palliative care is associated with improved outcomes for people living with terminal illness and their caregivers. However, the lack of studies based in low- and middle-income countries limits the generalizability of the results and prevents conclusions as to whether similar interventions will have the same outcome outside high-income 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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".