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Care transition strategies in Latin American countries: an integrative review

2018· review· en· W2898538162 on OpenAlexaff
Maria Alice Dias da Silva Lima, Ana María Müller de Magalhães, Nelly D. Oelke, Giselda Quintana Marques, Elisiane Lorenzini, Luciana Andressa Feil Weber, Iris Fan

Bibliographic record

VenueRevista gaúcha de enfermagem · 2018
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsCINAHLScopusMultidisciplinary approachLatin AmericansPortuguesePromotion (chess)MedicineMEDLINENursingSciELOTransition management (governance)Transition (genetics)Health careElectronic libraryPolitical sciencePsychological interventionBusinessDigital library

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify and analyze available literature on care transition strategies in Latin American countries. METHODS: Integrative literature review that included studies indexed in PubMed, LILACS, Web of Science Core Collection, CINAHL, SCOPUS databases, and the Scientific Electronic Library Online (SciELO), published in Portuguese, Spanish or English, between 2010 and 2017. RESULTS: Eleven articles were selected and the strategies were grouped into components of care transition: discharge planning, advanced care planning, patient education and promotion of self-management, medication safety, complete communication of information, and outpatient follow-up. These strategies were carried out by multidisciplinary team members, in which nurses play a leading role in promoting safe care transitions. CONCLUSIONS: Care transition activities are generally initiated very close to patient discharge, this differs from recommendations of care transition programs and models, which suggest implementing care transition strategies from the time of admission until discharge.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.396
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations77
Published2018
Admission routes1
Has abstractyes

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