Nurse as a Facilitator to Professional Communication: A Qualitative Study
Bibliographic record
Abstract
Nurses need to establish communication with other healthcare professionals to facilitate the process of care. Healthcare professionals have complementary roles in providing care to patients. As the key members of the healthcare team, nurses also have an important role in establishing communication among other healthcare professionals. The final outcome of professional communication is effective care and improved patient outcomes. The aim of this study was to explore nurses' role in establishing professional communications with other healthcare professionals. This was a descriptive qualitative study. The study was conducted by using the content analysis approach. A purposive sample of sixteen healthcare professionals was recruited from six teaching hospitals affiliated to Tehran University of Medical Sciences, Tehran, Iran. Study data were gathered by conducting personal face-to-face semi-structured interviews and were analyzed by using the qualitative content analysis approach. The three main themes of the study were 'Nurse as the mediator of communication', 'Nurse as the executor of others' duties, and 'Nurse as a scapegoat'. Study findings can be used by nurses, managers, and health policy-makers to develop effective strategies for exactly determining and clarifying nurses and other healthcare professionals' roles as well as for informing the public and other healthcare professionals about nurses' roles and importance.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".