MétaCan
Menu
Back to cohort

<b> Perfil dos diagnósticos de enfermagem de pacientes internados em unidade de clínica médica/ Profile of nursing diagnoses of patients hospitalized at a medical clinic unit<b>

2016· article· pt· W2530600563 on OpenAlex
Emilia Batista Lopes, Jussara Simone Lenzi Pupulim, Ana Paula Vilcinski Oliva

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCiência Cuidado e Saúde · 2016
Typearticle
Languagept
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineNursing careGerontologyNursing

Abstract

fetched live from OpenAlex

O objetivo foi identificar a frequência dos diagnósticos de enfermagem em clientes hospitalizados em unidade de clínica médica. Tratou-se de um estudo quantitativo do tipo descritivo-exploratório realizado na unidade de clínica médica do Hospital Universitário Regional de Maringá (HURM) com amostra de 25 participantes. Dos 13 domínios descritos pela NANDA-I, todos foram representados por pelo menos um diagnóstico. Foram levantados 530 diagnósticos, com uma média de 21,2 por paciente. Os diagnósticos predominantes foram risco de infecção (100%), integridade da pele prejudicada (88%), manutenção ineficaz da saúde (76%), deambulação prejudicada (76%), conforto prejudicado (76%), padrões de sexualidade ineficazes (72%), mobilidade física prejudicada (68%), integridade tissular prejudicada (68%), déficit no autocuidado para banho (64%), para higiene íntima (64%), para vestir-se (64%) e mobilidade prejudicadano leito (60%). Esses resultados contribuíram para a identificação das necessidades mais afetadas dos pacientes internados facilitando a elaboração de planos de cuidados de enfermagem mais eficazes.

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.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.026
GPT teacher head0.333
Teacher spread0.307 · 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