Use of home telehealth in palliative cancer care: a case study
Bibliographic record
Abstract
We conducted a mixed-methods case study to explore the perceptions of family caregivers and palliative cancer patients of home telehealth, and their experience with it. The intervention in the randomized controlled trial from which study participants were selected consisted of specialist nurses available 24 hours per day who communicated with patients and families using videophones, with optional remote monitoring. Qualitative data were collected from interviews with five patient/caregiver dyads and seven bereaved family caregivers, direct observation and nursing documentation. Quantitative data were collected from computerized nursing documentation and analyzed for patterns of use. During the study there were 255 contacts, including videophone, telephone or face-to-face visits, between tele-nurses and families. Overall the patients, family caregivers and tele-nurses felt that home telehealth enabled family caregiving, citing increased access to care, and patient and family caregiver reassurance. Pain management was the most common reason for initiating contact with the nurse, followed by emotional support. Concerns included lack of integration of services, inappropriate timing of the intervention and technical problems. The case study confirmed the importance of timely and accessible care for a group of clinically vulnerable, dying cancer patients and their family caregivers.
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.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".