Professional Development Needs of Novice Nursing Clinical Teachers: A Rapid Evidence Assessment
Bibliographic record
Abstract
The current nursing profession is challenged with a decreasing supply of competent clinical teachers due to several factors consequently impacting the quality of nursing education. To meet this demand, academic nursing programs are resorting to hiring expert nurses who may have little or no teaching experience. They need support during their transition from practice to teaching. Using the systematic approach of a Rapid Evidence Assessment (REA), scholarly literature was reviewed to identify existing professional development needs for novice clinical teachers as well as supportive strategies to aid the transition of experienced nurses into teaching practice. The REA included 29 relevant studies. Findings revealed three main professional development needs for novice clinical teachers and key supportive strategies. Based on these findings recommendations for best practices to support and prepare novice clinical teachers are presented.
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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 | 0.098 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".