Lessons on COVID-19 from Home and Community: Perspectives of Nursing Leaders at All Levels
Bibliographic record
Abstract
The initial focus of the COVID-19 pandemic was on the surge capacity of hospitals. Moving forward, however, the attention needs to shift toward keeping people healthy at home. In this paper, we discuss critical insights from the home and community care sector, which shed light on pre-pandemic fault lines that have widened. The paper, however, takes a positive look at how a better future can be built, particularly for those most vulnerable in society. We offer three key insights and analyses as well as examples of how one national homecare organization in Canada, SE Health, is facing the pandemic. We discuss the following key insights: (1) pre-pandemic systemic biases and barriers were exasperated during the pandemic, which impacted the most vulnerable; (2) nurse leaders were faced with unprecedented fear and anxiety from both patients and their staff colleagues; and (3) the pandemic provided an opportunity for significant learning, innovation and capacity development. The pandemic is far from over - we are in a marathon, not a sprint. The paper concludes with how nurse leaders can lead the way in navigating through the pandemic and build a better "new normal."
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.020 |
| 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".