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Record W2061815273 · doi:10.1177/1084822314547960

Promoting the Use of Visiting Nurse Services for Patients Discharged From Hospital

2014· article· en· W2061815273 on OpenAlexaboutno aff
Satoko Nagata, Kaoru Ogawa, Atsuko Taguchi, Takashi Naruse, Sachiyo Murashima, Joan K. Magilvy

Bibliographic record

VenueHome Health Care Management & Practice · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNursingQuarter (Canadian coin)Hospital readmissionHospital dischargePsychologyMedical emergencyFamily medicineIntensive care medicine

Abstract

fetched live from OpenAlex

A project to encourage the adequate use of visiting nurse services (VNS) after discharge was conducted by “K” City, Japan. Eligible individuals were inpatients of general hospital ready for discharge and assessed as potentially needing VNS, and their out-of-pocket fees for VNS within 1 month following discharge were covered by the city. To clarify this project’s effect, patient/family’s intention to use VNS and the utilization of VNS at each time point were evaluated. Eighty-eight residents used the project. Before initial hospitalization, less than one quarter of users had recognized the necessity of VNS. Many participants (88.6%) continued to use VNS even after the completion of the project period despite having to cover the VNS cost by themselves, indicating that this project had a positive impact on appropriate VNS 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.365
Teacher spread0.344 · 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.

Study designNot applicable
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

Citations0
Published2014
Admission routes1
Has abstractyes

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