Communication quality improvement in student nursing clinicals
Bibliographic record
Abstract
Background: Little previous research has examined attempts to improve the quality of communication among nursing clinical students, unit-based educators, and academic educators. The current study utilized focus groups and needs assessments to identify communication concerns of both academic and unit-based clinical educators in several inpatient settings. Methods: Quality improvement interventions were developed based on concerns and needs identified by staff. The interventions included zone phones, concise student placement summaries, and unit communication boards. Comparisons of pre- and post- intervention surveys of unit staff and of academic faculty were conducted by t -tests. Results: Statistical analyses indicated areas of significant communication improvements between academic faculty and both students and unit staff. Interventions did not show significant benefits for communication between unit staff and students. Conclusions: Application of quality improvement techniques resulted in successful improvement of communications among nursing students, clinical site educators, and academic educators. The results underscore the need to further tailor and evaluate quality improvement efforts at the level of day-to-day patient care, and to address the inevitable diversity among hospital units via timely staff input on the most effective unit-level interventions.
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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 | 0.011 | 0.051 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".