COMMUNICATION COMPETENCY AND EMPATHY AMONG NURSES IN THE INTENSIVE CARE UNIT OF TERTIARY HOSPITALS IN SAUDI ARABIA
Bibliographic record
Abstract
Intensive Care Unit (ICU) is a highly stressful and unique environment. Caring for a diverse client population requires nurses effective communication and empathy. These traits, in turn, help to improve patient care and outcomes, increase the level of patient satisfaction, and decrease adverse events. These also promote effective decision making, problem-solving, and encourage good collaboration with fellow health workers. The purpose of the present study is to determine the relationship between Communication Competence and Empathy among Nurses in the Intensive Care Unit of Tertiary Hospital in Saudi Arabia. The respondents consisted of 243 ICU nurses, and the tools used were a Self -Perceived Communication Competence Scale and Toronto Empathy Questionnaire. Descriptive correlational statistics were used to analyze the data. Results revealed that the Self-Perceived Communication Competence among nurses in the ICU is High in contexts such as in public and in group with strangers, friends and acquaintances; and the nurses have high level of empathy. It was also revealed in the results that level of communication competence and level of empathy have no significant relationship. However, the level of communication competence is significantly linked to educational attainment of nurses; while the level of empathy is significantly correlated to age , nationality and length of experience of the nurses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".