Pengaruh Pelatihan Komunikasi terhadap Kompetensi Klinis Perawat dan Kepuasan Pasien di RS Pangkalpinang
Bibliographic record
Abstract
This study aims to analyze the impact of communication training on nurses’ clinical competence and patient satisfaction at RS Pangkalpinang. Effective communication is a critical component in enhancing healthcare quality, particularly in nurse–patient interactions. A quantitative approach with a survey method was employed, involving 53 nurses who had completed structured communication training and 25 patients treated by those nurses. Data were analyzed using simple and multiple linear regression with SPSS version 26. The results showed a significant improvement in nurses’ clinical competence, with mean scores increasing from 72.4 (pre-test) to 85.7 (post-test), particularly in the domain of therapeutic communication. Patient satisfaction scores also increased from an average of 78.2 to 88.9 after the training intervention. Regression analysis revealed that communication training had a significant positive effect on nurses’ clinical competence (R² = 0.48; p < 0.001) and on patient satisfaction (R² = 0.41; p < 0.01). These findings suggest that structured communication training grounded in theoretical models such as Peplau’s Interpersonal Theory and the Calgary-Cambridge Guide can enhance professional competence and improve patient perceptions of care. The practical implication is that hospitals should integrate communication training into ongoing staff development programs and consider its replication in other healthcare settings to improve the overall quality of nursing services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".