Infection prevention and control for diverse vulnerable populations: From an emergency response to the COVID-19 pandemic to sustainable improvement
Bibliographic record
Abstract
At the onset of the COVID-19 pandemic in early 2020, organizations providing residential and respite care for individuals with developmental disabilities and complex care needs in the Greater Toronto Area were largely unprepared. As case numbers surged, they lacked the expertise and resources needed to prevent spread across populations that are highly vulnerable to infection and poor outcomes. This article describes how these organizations, led by Safehaven, responded to an unprecedented emergency, and how the response is leading to sustainable improvements in care and safety for diverse vulnerable groups in congregate care settings. As the pandemic advanced, the Safehaven Program evolved with the solidification of the role of Infection Prevention and Control Champion lead role in Ontario and partnership with Reena in York Region.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".