A summary of reported infection prevention and control assessments conducted in long-term care and retirement homes during COVID-19 pandemic in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Public Health Ontario (PHO) infection prevention and control (IPAC) specialists provided field support during the COVID-19 pandemic by conducting on-site and virtual IPAC assessments to long-term care and retirement homes (LTC/RHs) between April 2020 and June 2021. Reports from these IPAC assessments were analyzed and the most common challenges were identified. Methods: IPAC specialists in collaboration with local public health units (PHU) conducted 139 on-site and 33 virtual visits to LTC/RHs in Ontario, using an assessment tool developed by PHO. Following each assessment, a report with findings and recommendations for enhancing IPAC practices in the LTC/RHs were shared with the home and PHU. A thematic analysis of the reports found common challenges in several areas. Results: Analysis of 172 assessment reports identified challenges and gaps in several areas resulting in a total of 415 recommendations made to LTC/RHs. Recommendation areas addressed included: personal protective equipment (PPE) use – 115 (28%), screening process – 89 (21%), physical distancing – 66 (16%), environmental cleaning –66 (16%), hand hygiene – 44 (11%), cohorting – 26 (6%), and other areas – 9 (2%). Inappropriate use and reuse of PPE, such as universal wearing of full droplet and contact PPE regardless of resident COVID-19 status and double masking were observed. Other common themes included incorrect screening and physical distancing practices, and improper use of the disinfecting wipes and cleaning products. Often, there was no defined process for cleaning high-touch surfaces or tracking when cleaning had occurred and deficiencies in the auditing process were noted.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it