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Record W3128615172 · doi:10.23880/nhij-16000217

Cross-Cultural Adaptation of the Vancouver Scar Scale - Baryza Version to Brazilian Portuguese

2020· article· en· W3128615172 on OpenAlexaboutno aff
Costa PTL

Bibliographic record

VenueNursing & Healthcare International Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsBrazilian PortuguesePortugueseCross-culturalScale (ratio)Adaptation (eye)GeographyCartographyPsychologySociologyAnthropologyLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Objective: To perform the cross-cultural adaptation of the Vancouver Scar Scale -Baryza Version to the Brazilian population with burns.Method: Methodological study carried out in seven steps: 1) translation of the original scale into two versions; 2) summary of translations; 3) evaluation by expert committee; 4) back translation in two versions; 5) summary of back translations; 6) comparison with the original scale; and 7) semantic validation.The 18 participants were invited by email based on strict criteria determined for each stage.Results: The steps culminated in an instrument which did not need any modifications according to the original author of the scale, which was then subjected to semantic analysis, which caused difficulties in translation especially in relation to the vascularization and pigmentation items, but the evaluators rated the overall impression as good and very important for the condition. Conclusion:The preliminary version derived from the methodological pathway presents adequate semantic validity and the instrument proves to be suitable for future studies, pretesting and evaluation of its psychometric properties.

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)0.005
metaresearch head score (Gemma)0.015
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.031
GPT teacher head0.357
Teacher spread0.326 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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