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Record W2083624501 · doi:10.1258/135763307780096203

Analysis of the suitability of 'video-visits' for palliative home care: implications for practice

2007· article· en· W2083624501 on OpenAlexaffabout
Marilynne Hebert, Marie-Josée Paquin, Lynn Whitten, Pin Cai

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2007
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAlberta Health ServicesAlberta Cancer FoundationUniversity of Calgary
Fundersnot available
KeywordsMedicinePalliative careMedical recordPsychological interventionFamily medicineHealth careNursing

Abstract

fetched live from OpenAlex

We conducted a retrospective chart review to estimate the extent to which palliative home care visits could be carried out using videophones and to explore factors that might inform the eligibility criteria for video-visits. Four hundred palliative home care health records of deceased clients from 2002 were randomly selected from the Health Records Office in one Canadian health region. One visit was randomly selected from each of these health records. Three hundred and fifty-four visits were coded, and based on professional nursing judgment, the coder estimated whether video-visits could have been carried out. Approximately 43% of the visits were considered appropriate for video-visits. The results suggest that four factors may inform eligibility and decisions about a client's suitability for video-visits: diagnosis (cancer versus non-cancer), low Edmonton Symptom Assessment System (ESAS) score, no care-giver present, number and types of interventions required. Patients with a cancer diagnosis were more likely to be suitable for video-visits, which suggests that disease trajectory, rather than diagnosis of 'palliative', may be more influential in determining the care required and appropriateness of videophone use.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.040
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.076
GPT teacher head0.450
Teacher spread0.375 · 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

Citations33
Published2007
Admission routes2
Has abstractyes

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