We Weren’t Prepared – Rethinking the Design of Long-Term Care Homes for Infectious Outbreaks
Bibliographic record
Abstract
Canada’s Long-Term Care (LTC) sector was severely burdened by the coronavirus disease (COVID-19) pandemic, accounting for 81% of COVID-19 deaths nationwide as of June 2020, and 43% of deaths at the end of 2021 despite widespread vaccination. Ontario’s Science Table on COVID-19 identified 5 priorities moving forward which included improving staffing, essential caregiver access, timely/ high-quality palliative care, building/ maintaining infection prevention and control (IPC) expertise in homes, and rethinking the design of LTC homes. Given the frequent contact among residents, staff, and caregivers/ family members in homes, supporting IPC through design is an important strategy to ensure the quality of life (QoL) and care of residents, and the occupational health and safety (OHS) of healthcare workers. To support the ‘design priority’ from Ontario’s Science Table, we studied how the design of 8 LTC homes in Ontario influenced IPC, QoL, and OHS during the pandemic through photo diaries and interviews/ focus groups (N=38). We then developed alternative home concepts through co-design sessions with participants. We found deficiencies in the design of entrances/ exits, resident rooms, shared resident areas, outdoor areas, staff work areas, storage/ supply areas, soiled/ clean areas, design to support the donning/ doffing of personal protective equipment, among other issues. From this we developed design recommendations and concepts that may help inform how we can better respond to infectious outbreaks while balancing the QoL of residents, staff members, and caregivers/ family members.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".