French Canadian translation and the validity and inter-rater reliability of the ISTAP Skin Tear Classification System
Bibliographic record
Abstract
OBJECTIVE: To adapt the International Skin Tear Advisory Panel (ISTAP) skin tear classification system into French Canadian, and to test the content validity and inter-rater reliability of the translated version. METHOD: Phase one included the translation of the ISTAP skin tear classification system into French Canadian, using a forward-back translation method. Following this the translated version was tested for content validity and inter-rater reliability with registered nurses from a French acute care hospital in Ottawa, Canada. RESULTS: The French Canadian translation of the ISTAP skin tear classification system was evaluated by 92 nurses without in-depth wound care training. The adapted version obtained a substantial level of agreement between users, (Fleiss' Kappa = 0.69). CONCLUSION: The study tested the content validity and inter-rater reliability of the French Canadian version of the ISTAP skin tear classification system. The results support previous studies and further validate the classification system as a reliable method for classifying skin tears. The study supports ISTAP's goal of establishing a global language for describing and documenting skin tears.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".