Decommissioning and recommissioning a regional hospital in response to a COVID-19 outbreak
Bibliographic record
Abstract
CONTEXT: The COVID-19 outbreak at the North West Regional Hospital (NWRH) site in Tasmania, Australia in April 2020 was both rapid and tragic. Within 10 days of identification of the first healthcare worker infection, both hospitals had closed, and all patients were discharged or decanted to other facilities within the state. The entire hospital staff (approximately 1300 people) and their households (approximately 3000-4000 people) were furloughed for 14 days to halt the spread of infection. During the furlough period, a decommissioning, terminal clean and recommissioning process was undertaken alongside recovery and reorientation of the workforce to personal protective equipment. Within 4 days of closure, an Australian Defence Force and Australian Medical Assistance Team team opened the prioritised emergency department to provide emergency care for the local community, supported by modified diagnostic services. The decommissioning and cleaning rolled on over the ensuing month, in a predetermined priority order. As staff returned from quarantine, they recommissioned their clinical areas. The final ward, a modified medical isolation wing, reopened on day 29. ISSUE: Disaster management activities may be grouped under four main headings: prevention, preparedness, response and recovery. There are many opportunities for improvement and learning, and this article focuses on the local response and recovery, describing the process undertaken from the perspective of a small management group. Authors CC, HE, TB and MW were on the ground during the decommissioning process, then managed aspects of the cleaning and recommissioning remotely from furlough. Authors TA and TC provided specialist IPC support and developed education remotely. LESSONS LEARNED: Almost 2 months on, no new COVID-19 infections had been reported. The aim of this article is to provide a foundation for site-specific adaptation to include in pandemic escalation plans in other regional and rural settings.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".