Enhancing communication between nurses and patients in ambulatory care settings: A pretest–posttest design
Bibliographic record
Abstract
Background and objective: In ambulatory care settings, effective nurse–patient communication is often hindered by workload pressures and time constraints. A major contributing factor is the lack of nurses’ communication-related knowledge, attitudes, and skills, which affects their ability to engage meaningfully with patients and deliver high-quality care. This study aims to evaluate the effectiveness of structured interventions in improving nurses’ communication-related knowledge, attitudes, and skills, with the goal of achieving over 95% compliance. Additionally, the study seeks to enhance patient satisfaction to at least 95% within ambulatory care settings. Methods: This study employed a descriptive cross-sectional design to assess patient satisfaction and a one-group pretest–posttest design to evaluate the impact of the interventions on nurses’ communication with patients. Results: Nurses’ overall communication compliance increased from 89.85% in the pre-test to 99.03% in the post-test, reflecting significant improvements across the knowledge, skills, and attitude domains. Patient satisfaction also demonstrated a substantial rise, increasing from 77.6% before the interventions to 97.92% afterward. Conclusions: The implementation of structured interventions—including communication skill development, team collaboration, ongoing training, and motivational initiatives—led to significant improvements in nurses’ communication practices and a marked increase in patient satisfaction within ambulatory care settings. These findings underscore the value of continuous, well-designed communication training in fostering effective nurse–patient interactions and enhancing the overall patient experience.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".