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Record W2889205221 · doi:10.1590/0034-7167-2017-0029

Cultural adaptation of the Pieper-Zulkowski Pressure Ulcer Knowledge Test for use in Brazil

2018· article· en· W2889205221 on OpenAlexaff
Soraia Assad Nasbine Rabeh, Simon Palfreyman, Camilla Borges Lopes Souza, Rodrigo Magri Bernardes, María Helena Larcher Caliri

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCronbach's alphaBrazilian PortuguesePortugueseFace validityTest (biology)Internal consistencyEquivalence (formal languages)European PortugueseAdaptation (eye)MedicinePsychologyApplied psychologyClinical psychologyPsychometricsMathematicsLinguistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To carry out the cultural adaptation of the Pieper-Zulkowski Pressure Ulcer Knowledge Test (PZ-PUKT) for use in Brazil and analyze the internal consistency of the adapted version. METHOD: This was a methodological study. The PZ-PUKT is a knowledge test consisting of 72 items, divided into: prevention, staging, and wound description. The present study was developed in two phases: (1) translation of the questionnaire from English to Brazilian Portuguese, back-translation, and assessment of equivalence between the original and back-translated version by an expert panel; (2) pre-testing with nurses. RESULTS: The questionnaire showed face and content validity according to the opinions of the experts. Cronbach's alpha for the total test score was higher than 0.70. The adapted version presented satisfactory internal consistency for the studied sample. CONCLUSION: The adapted version of the instrument for Portuguese can be used in intervention studies as a tool to measure "nursing knowledge about pressure injury/ulcers" as a dependent variable.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.578

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 communication0.0000.000
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 head0.115
GPT teacher head0.433
Teacher spread0.318 · 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 teacher head, 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

Citations36
Published2018
Admission routes1
Has abstractyes

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