Nurse Practitioners Navigating the Consequences of Directives, Policies, and Recommendations Related to the COVID-19 Pandemic in Long-Term Care Homes
Bibliographic record
Abstract
OBJECTIVES: New models for the workforce are required in long-term care (LTC) homes, as was made evident during the Coronavirus Disease 2019 (COVID-19) pandemic. Nurse Practitioner (NP)-led models of care represent an effective solution. This study explored NPs' roles in supporting LTC homes as changes in directives, policies, and recommendations related to COVID-19 were introduced. DESIGN: Qualitative exploratory study. CONTEXT: Thirteen NPs working in LTC homes in Ontario, Canada. METHODS: Semi-structured interviews were conducted in March/April 2021. A five-step inductive thematic analysis was applied. FINDINGS: Analysis generated four themes: leading the COVID-19 vaccine rollout; promoting staff wellbeing related to COVID-19 fatigue; addressing complexities of new admissions; and negotiating evolving collaborative relationships. CONCLUSIONS: Nurse practitioners were instrumental in supporting LTC homes through COVID-19 regulatory changes producing unintended consequences. The NPs' leadership in transforming care is equally essential in LTC homes as in other established healthcare settings, such as primary and acute care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".