Translation and validation of the Cultural Competence Self-assessment Checklist of Central Vancouver Island Multicultural Society for Health Professionals
Bibliographic record
Abstract
The World Health Organisation emphasizes the importance of training future healthcare practitioners to practice respectful and person-centred health care. The importance of this can be demonstrated in the example of cultural competence, which has been observed to be associated with improved patient satisfaction and concordance with recommended treatment. The aim of this study was to translate and validate in the Greek language the Cultural Competence Self-assessment Checklist of the Central Vancouver Island Multicultural Society and test it in the population of health scientists in Cyprus. A cross-sectional analysis took place between October 2021 and January 2022 in 300 health scientists in Cyprus using convenient sampling. The sample consisted of doctors, nurses, psychologists, social workers and physiotherapists. In order to test the questionnaire internal consistency reliability we used the Cronbach coefficient alpha. After the translation of the Cultural Competence Self-assessment Checklist there was a Cronbach alpha indication of 0.7 in all three thematic units of the checklist. 300 participants filled in the research tool, 241 women (80.3%) and 59 men (19.6). Only 2% of the sample had attended a cultural competence training before or had an expertise. The Greek version of the Cultural Competence Self-assessment Checklist of the Central Vancouver Island Multicultural Society is a valid instrument that can be used in the Greek language referring to health scientists both in Cyprus and Greece. Keywords: Cultural Competence, health professionals, validation in Greek, self assessment.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".