Translation and cultural adaptation of the Nurse Professional Competence Scale: The NPC Scale – German AUT language version
Bibliographic record
Abstract
Assessing nursing-related competences becomes increasingly relevant. Therefore, psychometrically tested and contentual appropriate instruments are needed. The Nurse Professional Competence (NPC) Scale seems convenient to assess nurses' competences in German-speaking countries. This article describes the translation and cultural adaption of the NPC Scale English-language version for the German-speaking linguistic area of Austria (AUT), following the respective principles defined by the International Society for Pharmaoeconomics and Outcome Research (ISPOR). The aim was to provide a German-language version of the NPC Scale usable for the Austrian specific linguistic and cultural area. Due to polydimensionality of the scale and the extensive number of items being stepwise revised by researchers, several innovative methodological approaches were required to ensure transparent and comprehensible decision-making, data-revision and consensus-gainig throughout the overall process. Useful methods are presented to cope with challenges accompanying the coverage of decentralized data-revision and consensus-finding within the translation and cultural adaption of a polydimensional scale with a high number of items. The Nurse Professional Competence Scale, 88 items – German Austrian language version is conceptually, semantically and idiomatically equivalent compared to the NPC Scale original version and is recommendable for the usage in the target country's nursing context from a linguistic point of view.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".