Nurse Practitioners Rising to the Challenge During the Coronavirus Disease 2019 Pandemic in Long-Term Care Homes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is an urgency to respond to the longstanding deficiencies in health human resources in the long-term care (LTC) home sector, which have been laid bare by the coronavirus disease 2019 (COVID-19) pandemic. Nurse practitioners (NPs) represent an efficient solution to human resource challenges. During the current pandemic, many Medical Directors in LTC homes worked virtually to reduce the risk of transmission. In contrast, NPs were present for in-person care. This study aims to understand the NPs' roles in optimizing resident care and supporting LTC staff during the pandemic. RESEARCH DESIGN AND METHODS: This exploratory qualitative study employed a phenomenological approach. A purposive sample of 14 NPs working in LTC homes in Ontario, Canada, was recruited. Data were generated using semistructured interviews and examined using thematic analysis. RESULTS: Four categories relating to the NPs' practices and experiences during the pandemic were identified: (a) containing the spread of COVID-19, (b) stepping in where needed, (c) supporting staff and families, and (d) establishing links between fragmented systems of care by acting as a liaison. DISCUSSION AND IMPLICATIONS: The findings suggest that innovative models of care that include NPs in LTC homes are required moving forward. NPs embraced a multitude of roles in LTC homes, but the need to mitigate the spread of COVID-19 was central to how they prioritized their days. The pandemic clearly accentuated that NPs have a unique scope of practice, which positions them well to act as leaders and build capacity in LTC homes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".