Understanding cultural competence in a multicultural nursing workforce: registered nurses' experience in Saudi Arabia.
Bibliographic record
Abstract
PURPOSE: In Saudi Arabia, the health system is mainly staffed by expatriate nurses from different cultural and linguistic backgrounds. Given the potential risks this situation poses for patient care, it is important to understand how cultural diversity can be effectively managed in this multicultural environment. The purpose of this study was to explore notions of cultural competence with non-Saudi Arabian nurses working in a major hospital in Saudi Arabia. DESIGN: Face-to-face, audio-recorded, semistructured interviews were conducted with 24 non-Saudi Arabian nurses. Deductive data collection and analysis were undertaken drawing on Campinha-Bacote's cultural competence model. The data that could not be explained by this model were coded and analyzed inductively. FINDINGS: Nurses within this culturally diverse environment struggled with the notion of cultural competence in terms of each other's cultural expectations and those of the dominant Saudi culture. DISCUSSION: The study also addressed the limitations of Campinha-Bacote's model, which did not account for all of the nurses' experiences. Subsequent inductive analysis yielded important themes that more fully explained the nurses' experiences in this environment. IMPLICATIONS FOR PRACTICE: The findings can inform policy, professional education, and practice in the multicultural Saudi setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".