Development of communication skills in healthcare: Perspectives of new graduates of undergraduate nursing education
Bibliographic record
Abstract
Background : Communication between healthcare providers and patients affects patient care and safety. Nurses develop communication skills at the undergraduate level, after graduation and throughout their practice. This study explored perspectives of new graduates on the development and implementation of communication skills within the first six months after graduation. Methods : Descriptive study using an electronic survey to three cohorts of recent graduates. Surveys were sent within six months after graduation. Results : A total of 700 surveys were sent to three cohorts of recent graduates with 206 surveys completed (response rates ranged from 26%-35%). Participants reported always feeling confident communicating with patients and families 27% of the time, and 23.5% of the time with interdisciplinary teams. Only 50.8% reported always feeling confident to provide safe care all of the time with 44.5% reported feeling always able to ask colleagues for help with challenging situations. Conclusions : Undergraduate nursing programs should incorporate more didactic communication skills training and simulation to prepare nursing students and increase their confidence to provide safe care and consult with colleagues for help. Communication skills training in practice settings after graduation to increase skill development and confidence during independent clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".