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Record W1987340441 · doi:10.1258/jtt.2012.111201

Use of home telehealth in palliative cancer care: a case study

2012· article· en· W1987340441 on OpenAlexaff
Anita Stern, Ruta Valaitis, Robin Weir, Alejandro R. Jadad

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2012
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsTelehealthMedicineFamily caregiversIntervention (counseling)NursingPalliative careFamily medicineDocumentationTelemedicineRandomized controlled trialHealth care

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.184
GPT teacher head0.463
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations61
Published2012
Admission routes1
Has abstractyes

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