The Creation of a Novel Undergraduate Nursing Employee/Student Hybrid Role in the COVID-19 Response
Bibliographic record
Abstract
The COVID-19 pandemic impacted nursing education and health care systems alike. Increases in staff absenteeism along with increased hospitalizations have strained health systems across the globe. Postsecondary institutions (PSIs) were required to remove students from clinical placements, thus delaying nursing students' ability to complete their programs, and in turn, contributing to the nursing workforce challenges. Health care organizations and PSIs had to collaborate innovatively to support the health care response to the pandemic while continuing to educate and graduate students to expand the nursing workforce. In Alberta, the collaboration between the health system and PSIs led to the creation of an undergraduate nursing employee/student hybrid (UNE/Hybrid) role. This role was not only a response to the nursing workforce challenges created by the pandemic, but it provided nursing students with positive learning clinical placements ensuring that they completed their program in a timely manner. This role was designed to assist with the fourth wave of the pandemic (omicron variant), which was expected to be the most severe wave in terms of hospitalizations and increased staff absences. The UNE/Hybrid role allowed nursing students to complete the required learning for their final preceptorships and/or complete leadership placements in a paid role while being integrated into the unit culture and becoming part of the team. The initiative's results, including its successes, challenges, and lessons, are discussed.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".