Pandemic Responsiveness in an Acute Care Setting: A Community Hospital’s Utilization of Operational Resources During COVID-19
Bibliographic record
Abstract
Jesse McLean,1 Cathy Clark,1 Aidan McKee,1,2 Suzanne Legue,1 Jane Cocking,1 Amanda Lamarche,1 Corey Heerschap,1 Sarah Morris,1 Tracey Fletcher,1 Corey McKee,1 Kristal Kennedy,1 Leigh Gross,1 Andrew Broeren,1 Matthew Forder,1 Wendy Barner,1 Chris Tebbutt,1 Suzanne Kings,1 Giulio DiDiodato1,3 1Royal Victoria Regional Health Centre, Barrie, ON, Canada; 2School of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland; 3Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, ON, CanadaCorrespondence: Giulio DiDiodato, Royal Victoria Regional Health Centre, 201 Georgian Drive, Barrie, ON, L4M 6M2, Canada, Email DidiodatoG@rvh.on.caBackground: 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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".