Case Study: The Impact of Nursing Professional Practice during the COVID-19 Pandemic at a Large Community Hospital in Canada
Bibliographic record
Abstract
The COVID-19 pandemic posed numerous challenges experienced by healthcare organizations. Nursing professional practice plays a crucial leadership role in supporting nursing staff and leaders in developing policies, parameters and philosophical approaches for delivering safe patient care. The professional practice leadership at Humber River Hospital, a large Canadian community hospital, implemented three key interventions in this hospital-based case study: (1) proactive workforce planning, (2) increased nursing student placements and (3) novel "stretch model of care" in the intensive care unit (ICU). The overall results following the implementation of these interventions resulted in substantial improvements. For example, proactive nursing workforce planning supported both a 98% reduction in agency utilization and an accelerated ICU certification program with an 84% certificate completion rate. Through innovative strategies, there was a significant increase (33-67%) in the number of nursing student placements during the first two years of the pandemic compared with previous years. Within the ICU setting, we maintained optimum ICU capacity that resulted in stronger partnership-driven relationships between nurses and physicians through an interprofessional "stretch model of care." Finally, we avoided emergency department closures and Code Orange calls during peaks of the pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".