Pandemic Responsiveness in an Acute Care Setting: A Community Hospital’s Utilization of Operational Resources During COVID-19
Bibliographic record
Abstract
Background: To ensure continuity of services while mitigating patient surge and nosocomial infections during the coronavirus disease 2019 (COVID-19) pandemic, acute care hospitals have been required to make significant operational adjustments. Here, we identify and discuss key administrative priorities and strategies utilized by a large community hospital located in Ontario, Canada. Methods: Guided by a qualitative descriptive approach, we performed a thematic analysis of all COVID-19-related documentation discussed by the hospital’s emergency operation centre (EOC) during the pandemic’s first wave. We then solicited operational strategies from a multidisciplinary group of hospital leaders to construct a narrative for each theme. Results: Seven recurrent themes critical to the hospital’s pandemic response emerged: 1) Organizational structure : a modified EOC structure was adopted to increase departmental interoperability and situational awareness; 2) Capacity planning : Design Thinking guided rapid infrastructure decisions to meet surge requirements; 3) Occupational health and workplace safety : a multidisciplinary team provided respirator fit-testing, critical absence adjudication, and wellness needs; 4) Human resources/workforce planning : new workforce planning, recruitment, and redeployment strategies addressed staffing shortages; 5) Personal protective equipment (PPE) : PPE conservation required proactive sourcing from traditional and non-traditional suppliers; 6) Community response : local partnerships were activated to divert patients through a non-referral-based assessment and treatment centre, support long-term care and retirement homes, and establish a 70-bed field hospital; and 7) Corporate communication : a robust communication strategy provided timely and transparent access to rapidly evolving information. Conclusion: A community hospital’s operational preparedness for COVID-19 was supported by inter-operability, leveraging internal and external expertise and partnerships, creative problem solving, and developing novel tools to support occupational health and community initiatives. Keywords: COVID-19, pandemic, infection, hospital, acute care, operational
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".