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Record W1958237348 · doi:10.5935/0034-7167.20140059

Concept analysis of the nursing outcome Mobility in nursing patients with stroke

2014· review· en· W1958237348 on OpenAlexaboutno aff
Rafaella Pessoa Moreira, Thelma Leite de Araújo, Tahissa Frota Cavalcante, Nirla Gomes Guedes, Marcos Venícios de Oliveira Lopes, Emília Soares Chaves

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2014
Typereview
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLHumanitiesMedicineNursingPsychologyArt

Abstract

fetched live from OpenAlex

This study aimed a concept analysis of the nursing outcome Mobility in patients with stroke. A literature review was conducted, through the online access to databases: Scopus, Pubmed, CINAHL, Cochrane, and Lilacs, using the descriptors: mobility, stroke, nursing and their synonyms in Portuguese and Spanish. 1.521 articles were identified, resulting in 49, after careful selection. Noteworthy are the articles published in Canada (26.7%), on 2001 (95.9%), by physiotherapists (34.6%), and in rehabilitation units (61.5%). The attributes identified for Mobility were: walking, standing, sitting, put the leg side to side, turn around, start and stop walking, stair climbing, motor function, and motor skill transfer. A model case and a contrary case were built, and identified, as antecedents: postural control and balance; and, as consequents: performs tasks inside and outside the house and wanders without difficulty. It was concluded that the concepts of Mobility found in this study need to be validated with experts in the field and in clinical practice.

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)0.009
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.481
Teacher spread0.392 · 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 designNot applicable
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

Citations4
Published2014
Admission routes1
Has abstractyes

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Same venueRevista Brasileira de EnfermagemSame topicHealth, Nursing, Elderly CareFrench-language works237,207