Integrating internationally educated nurses into the nursing faculty workforce: a new policy for nursing regulators
Bibliographic record
Abstract
Nursing faculty shortages received less attention in the literature and media outlets compared to registered clinical nursing staff shortages. One may question whether we do not have enough nursing faculty to teach and train students, who will take that responsibility? This critical question should be addressed by nursing leaders, researchers, and key system partners to develop innovative and sustainable policies that reduce nursing faculty shortages. Otherwise, the nursing faculty shortage would negatively affect the quality of nursing education and lead to a declining number of nursing seats, which should be avoided as we need more nurses in the upcoming years. This paper suggested developing a new policy for nursing regulators, titled “Non-clinical Academic Registration Category”, to support internationally educated nurses (IENs) with master's or doctoral degrees who wish to contribute to the nursing faculty workforce. To better understand the context of this policy and its benefits, the paper described the challenges of the registration process experienced by three IENs and the implications of integrating them into the workforce. Through collective and innovative policies, we can empower the future nursing faculty workforce and rationally respond to the ongoing crisis.
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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 | 0.058 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.036 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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".