Translation, adaptation, and content validation of a French version of the Nurse Competence Scale in Canada.
Bibliographic record
Abstract
Background: While everyone agrees that it is important for nurses to be competent practitioners, no validated French questionnaire measuring nurse competence is available to date. Internationally, one of the most frequently used questionnaires used to measure the competence level of nurses working in a clinical setting is the Nurse Competence Scale (NCS). Objective: The objective of this study was to translate and culturally adapt a French version of the NCS (NCS-Fr) with nurses working in the province of Quebec (Canada). Methods: It had a multi-method design, inspired by guidelines for translation, adaptation, and validation of scales in health research. The scale instructions and items were translated from English to French by two translators knowledgeable in nursing/healthcare and then back-translated to English by two other translators. Versions were compared; ambiguities and discrepancies were resolved during a synthesis discussion. A convenience sample of registered nurses (n=8) and experts in nursing education (n=10) assessed instructions and items for comprehensibility. Results: Content validity index (CVI) for items (I-CVI) of the preliminary version ranged from 0.56 to 1, with most items (n=64) meeting the threshold of 0.78. The scale CVI (S-CVI) was 0.89. Conclusion: This study used a rigorous method to translate and adapt a French version of the NCS. The next step will be to evaluate the psychometric properties and items performance of the NCSFr.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".