Focusing on fundamentals of care in an <scp>ICU</scp> setting during a pandemic
Bibliographic record
Abstract
AIMS: This manuscript aims to describe one acute care hospital's ICU journey during the COVID-19 pandemic and how fundamental care was central to the implementation of team-based models of care. BACKGROUND: Over the course of the COVID-19 pandemic, team-based and alternative models of care are being employed to manage and address global shortages and surge capacity. Employing these alternate models of care required attention to ensure fundamental care needs of patients were being met. DESIGN/METHOD: The following paper describes an ICU's journey of focusing on the delivery of the fundamentals of care through the implementation of team-based models of care to address the surge in patient care demands experienced in response to our global pandemic. CONCLUSIONS: The implementation of an evidence-informed approach to optimizing models of care and staffing in the ICU amid the evolving COVID-19 waves in one acute-care hospital is provided. This local approach focused on meeting patients' fundamental care needs throughout the necessary introduction of team-based care models and staffing changes and drew from evolving evidence, the ILC Fundamentals of Care Framework, and regulatory guidance.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".