Self-Assessment of Health Professionals’ Cultural Competence: Knowledge, Skills, and Mental Health Concepts for Optimal Health Care
Bibliographic record
Abstract
Current research often refers to cultural competence to improve health care delivery. In addition, it focuses on the cultural uniqueness of each health service user for optimal personalized care. This study aimed to collect self-assessment data from health professionals regarding their cultural competence and to identify their development needs. A mixed methods design was adopted using the Cultural Competence Self-assessment Checklist of the Central Vancouver Island Multicultural Society. This was translated into Greek, validated, and then shared with health professionals in Cyprus. Subsequently, a semi-structured interview guide was designed and utilized. This was structured in exactly the same question categories as the questionnaire. Data collection took place between October 2021 and May 2022, and convenience sampling was used to recruit 499 health scientists in Cyprus. The sample comprised doctors, nurses, psychologists, midwives, social workers, and physiotherapists. Subsequently, 62 interviews were conducted with participants from the same specialties. The results showed that (compared to other health professionals) nurses and psychologists are more sensitive to issues of cultural competence. It would appear that the more socially oriented sciences had better-prepared healthcare staff to manage diversity in context. However, there is a gap between knowledge and skills when comparing doctors to nurses; they seem to be more skilled and willing to intervene actively in cases of racist behavior or problem-solving. In conclusion, participants identified the importance of their cultural competence; they also realized the importance of optimal planning of personalized health care. There is a significant need for continuous and specialized cultural competence training for all health professions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".