Quantitative exploration of the barriers and facilitators to nurse-patient communication in Saudia Arabia
Bibliographic record
Abstract
Nurses with effective communication skills play a critical role in minimising the stress associated with hospitalisation for both patients and their families. Effective communication has become increasingly reported as a key component in effective health care outcomes, which is even more crucial in countries such as Saudi Arabia with a large foreign healthcare workforce. The presence of a large expatriate workforce with a different language from the host society and the ensuing complexity of sociocultural linguistic and heath beliefs systems has been poorly researched. This study aimed to investigate barriers and facilitators of nurse-patient communication in Saudi Arabia using the Nurses’ Self-Administered Communication Survey. The survey was distributed to a random sample of 291 nurses working in medical and surgical departments at five hospitals in Saudi Arabia. The results indicate that the Philippine and Saudi Arabian nurses perceived greater barriers to communication with respect to personal/social characteristics, job specifications and environmental factors then nurses of other nationalities. In addition, nurses with shorter experience in Saudi Arabia perceived greater barriers to communication with respect to the clinical situation of patient and environmental factors than the nurses with longer experience. Lastly, nurses who had not attended specialist courses on communication skills acquisition perceived greater barriers to communication with respect to personal characteristics and job specifications than nurses who had attended such courses. This study highlights the need to better prepare expatriate nurses before they enter the workforce in Saudi Arabia on cultural competence and language skills.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".