Evaluation of Barriers Contributing in the Demonstration of an Effective Nurse-Patient Communication in Educational Hospitals of Jahrom, 2014
Bibliographic record
Abstract
INTRODUCTION: Establishing an effective communication with patients is an essential aspect of nursing care. Nurse-patient communication has a key role in improving nursing care and increasing patient's satisfaction of health care system. The study aimed at evaluation of barriers contributing in the demonstration of an effective nurse-patient communication from their viewpoint. METHODS: This was cross-sectional study, carried out in 2014, with a sample of 200 nurses and patients drawn from two educational hospitals in jahrom city. Data were collected by using two questionnaire structured by the researchers. Data were analyzed using SPSS software (version 16). RESULTS: The results of this study showed that the greatest barriers of nurse-patient communication were characteristics of nursing job with an average score of 71.05 ± 10.18. The most communication barriers from patients viewpoint including: heavy work load of the nurses, age , sex and language difference between patient and nurse and the spicy morality of nurses. CONCLUSION: It is concluded that overcome barriers to communication and support are needed to enable nurses to communicate therapeutically with patients in order to achieve care that is effective and responsive to their needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".