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Evaluation of the nursing care model for children victimized of violence

2012· article· en· W2166709366 on OpenAlexaff
Patrícia Kuerten Rocha, Marta Lenise do Prado, Sherrill Conroy, Denise Maria Guerreiro Vieira da Silva

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicYouth, Drugs, and Violence
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNursingNursing careNursing Outcomes ClassificationPsychologyPropositionLegitimacyMedicinePrimary nursingNurse educationPolitical science

Abstract

fetched live from OpenAlex

The nursing care model represent important possibilities to develop the knowledge of nursing. However, there is no proposition that allow to verify the legitimacy of the care models. So, the objective of this study was to evaluate the Nursing Care Model for Children Victimized of Violence. The model was elaborated from a study of qualitative approach, the Convergent Assistant type. The study consists of evaluative study utilizing the instrument for evaluation of nursing care models, and conducted by a panel of 18 specialists. The analysis includes analysis of objective responses and other texts prepared by experts as part of its assessment process. The result shows that the Nursing Care Model, still needs tweaking and adaptations.

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)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
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 head0.431
GPT teacher head0.508
Teacher spread0.078 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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