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Record W7097848598

ral ssBioMed CentBMC Health Services Research Open AcceResearch article Patient, informal caregiver and care provider acceptance of a

2016· article· en· W7097848598 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)Home healthHealth careService providerHospital careHealth servicesMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Background: Hospital in the home programs have been implemented in several countries and have been shown to be safe substitutions (alternatives) to in-patient hospitalization. These programs may offer a solution to the increasing demands made on tertiary care facilities and to surge capacity. We investigated the acceptance of this type of care provision with nurse practitioners as the designated principal home care providers in a family medicine program in a large Canadian urban setting. Methods: Patients requiring hospitalization to the family medicine service ward, for any diagnosis, who met selection criteria, were invited to enter the hospital in the home program as an alternative to admission. Participants in the hospital in the home program, their caregivers, and the physicians responsible for their care were surveyed about their perceptions of the program. Nurse practitioners, who provided care, were surveyed and interviewed. Results: Ten percent (104) of admissions to the ward were screened, and 37 patients participated in 44 home hospital admissions. Twenty nine patient, 17 caregiver and 38 provider surveys were completed. Most patients (88%–100%) and caregivers (92%–100%) reported high satisfaction levels with various

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0320.001

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.

Opus teacher head0.059
GPT teacher head0.407
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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