An assessment of nurse-patient therapeutic communication and patient satisfaction with nursing care in multiple healthcare settings: A Study in Saudi Arabia
Bibliographic record
Abstract
Background: Effective therapeutic communication between nurses and patients is a fundamental element of high-quality healthcare. This study examines factors influencing therapeutic communication, including professional, contextual/situational, and patient-related aspects, while also assessing patient satisfaction with nursing care and the communication process.Methods: Employing a correlational cross-sectional design, a sample of 80 nurses and 99 patients under their care was selected using purposive sampling methods. This study encompassed diverse healthcare settings in Hail, Saudi Arabia. Data were collected through two survey questionnaires: the Nurse-Patient Therapeutic Communication Questionnaire for nurses and the Patient Satisfaction with Nursing Care Quality Questionnaire for patients. The data analysis was conducted using SPSS v29.0, with findings presented using descriptive and inferential statistics.Results: The professional dimension had a mean score of 5.56 ± 1.38, the contextual and situational dimension had a mean score of 5.69 ± 1.42, and the patient-related dimension had a mean score of 5.60 ± 1.46. Age, education level, and workplace significantly influenced all dimensions (all p < .001). Patient satisfaction scores ranged from 1.87 to 5.00, with an average score of 4.07 ± 0.72. Interestingly, patient satisfaction tended to increase with longer stays, r(97) = .23, p = .024, with the length of stay explaining 5.11% of the variability in patient satisfaction.Conclusions: This study identifies three key dimensions—professional, contextual/situational, and patient-related—as significant factors in nurse-patient communication. Demographic variables, including age, education, and workplace, also played pivotal roles. Notably, patient satisfaction levels were consistently high and positively correlated with longer stays. To foster patient-centred care, it is recommended to prioritize customized communication training and sustain nurturing interactions throughout the patient's care journey.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".