Promotion and tenure in nursing education: Lessons learned
Bibliographic record
Abstract
Current nursing faculty spend part of their careers in service settings before they move to the academic milieu. The promotion and tenure of process may not be familiar to novice nurse educators prior to their entry into the academy. Fewer nursing academics successfully achieve tenure than other disciplines outside of allied health. These low numbers have been attributed to underperformance in the domain of scholarship by these female professionals socialized to practice and service. This article explains the essential components of the tripartite process necessary for achieving success in the promotion and tenure process. The authors draw on their own experiences and that of their colleagues locally and nationally to demystify this career path. Intended as a primer on the promotion and tenure process for new nurse educators the article concludes with eight bulleted admonitions and commentary to inform and streamline successful achievement of career goals.
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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 | 0.044 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.018 |
| 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".