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Brazilian Validation of the Nursing Outcomes for Acute Pain

2012· article· en· W1569520048 on OpenAlex

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

VenueInternational Journal of Nursing Knowledge · 2012
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsMedicineNursing Outcomes ClassificationNursingDescriptive statisticsAcute careRelevance (law)Identification (biology)Nursing diagnosisMEDLINENursing researchHealth careMedical diagnosisTeam nursingStatistics

Abstract

fetched live from OpenAlex

PURPOSE: Validate the outcomes from the Nursing Outcomes Classification (NOC) for the Acute Pain nursing diagnosis. METHODS: The content validation of the seven NOC outcomes and their respective indicators was performed using an adaptation of Fehring's model and was analyzed by descriptive statistics. FINDINGS: Six were classified as critical and one was classified as supplemental. From the total of 118 indicators, 103 were validated. Of these, 27 were classified as critical and 76 as supplemental. CONCLUSIONS: The use of the NOC is a viable alternative for the assessment and identification of best practices in nursing care. CLINICAL RELEVANCE: Validation studies of nursing classifications corroborate the use of the component elements of these instruments in a variety of care settings.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.454

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 studies0.0000.000
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 head0.034
GPT teacher head0.415
Teacher spread0.381 · 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