Cultural adaptation and content validity of ISTAP Skin Tear Classification for Portuguese in Brazil
Bibliographic record
Abstract
Objective: To translate and culturally adapt the International Skin Tear Advisory Panel (ISTAP) Skin Tear Classification into the Portuguese language in Brazil and test the content validity of the adapted version. Methods: The cultural adaption comprised three phases: translation, evaluation by committee of judges composed of five stomatherapists (confirming the instrument content validity) and back-translation. Results: Two Brazilian Portuguese versions of the instrument were obtained after translation and analyzed by the committee, disagreements arose over several health related terms. This generated low values of the content validity index. However, the content validity was confirmed after discussion of discrepancies between the authors and some members of the judges’ committee, as well as with one of the authors of the original instrument, Dr. Kimberly LeBlanc, who also testified that validity when approving the back-translations of the adapted version to Brazilian Portuguese. Conclusion: The culturally adapted version of the ISTAP Skin Tear Classification is considered to have been obtained, with its content validity also attested. At that moment, the tests for inter and intraobserver reliability and concurrent validity are in the finalization phase, after which the instrument adapted and validated for Brazil will be made available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".