Impact of a dedicated education unit on nurse preceptors’ perceived confidence and competence
Bibliographic record
Abstract
The COVID-19 pandemic exacerbated the nursing shortage by increasing nurse departures from the nursing profession and limiting inpatient clinical education, which shifted significantly to online settings. Dedicated education units (DEUs), a form of academic-practice partnership, have been proposed to address both nursing staffing and clinical education problems. While prior research has emphasized DEU benefits for students, limited attention has been given to their impact on nursing preceptors. This study explored nurse preceptors' perceived confidence and competence following the implementation of a DEU. Qualitative design was employed using semi-structured interviews with six preceptors. Data was analyzed for common themes utilizing NVivo software. Findings indicated that nurse preceptors experienced improvements in perceived competence and confidence after the implementation of the DEU. Additional emergent themes were enhanced communication abilities and leadership skills in DEU nurse preceptors. Preceptors who perceived themselves as confident and competent were better positioned to promote resilience, support new graduate nurses, and contribute to safe, high-quality patient care. These results underscore the importance of supporting and developing nurse preceptors within DEUs to optimize outcomes for both nursing practice and clinical education.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".