Nursing lunch and learn program to support new faculty transitioning to academia: A pilot study
Bibliographic record
Abstract
Background and objective: Structured mentoring programs are crucial for new faculty transitioning to academia, especially nurses in clinical settings. The objective of this study was to evaluate a semi-structured Nursing Lunch and Learn Program (NLLP) among novice nursing faculty entering academia.Methods: This descriptive, cross-sectional pilot study described the development of the NLLP, its implementation, and evaluation. The NLLP was developed and implemented in a research-intensive University’s School of Nursing in the mid-Atlantic region of the United States during the 2022-2023 academic year. The faculty evaluated the program using self-reported surveys.Results: Among the 8-novice faculty, 6 female faculty completed the survey. Most faculty (n = 8, 75%) were advanced practice nurses entering academia from clinical practice. The NLLP was worthwhile, and participants suggested expanding beyond the School of Nursing to include other departments within the College. Among the 5 sessions, “Navigating the Appraisal and Promotion Process” was rated the highest.Conclusions: The NLLP was a successful new faculty orientation program aimed at fostering relationships among faculty and departmental leadership and increasing retention of novice faculty. This program assisted with the transition from clinical practice to novice faculty.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".