Characterization and evolution of infection control practices among severe acute respiratory coronavirus virus 2 (SARS-CoV-2)–infected healthcare workers in acute-care hospitals and long-term care facilities in Québec, Canada, Spring 2020
Bibliographic record
Abstract
OBJECTIVES: In this study, we aimed to (1) estimate the severe acute respiratory coronavirus 2 (SARS-CoV-2) infection rate and the secondary attack rate among healthcare workers (HCWs) in Québec, the most affected province of Canada during the first wave; (2) describe the evolution of work-related exposures and infection prevention and control (IPC) practices in infected HCWs; and (3) compare the exposures and practices between acute-care hospitals (ACHs) and long-term care facilities (LTCFs). DESIGN: Survey of cases. PARTICIPANTS: The study included Québec HCWs from private and public institutions with laboratory-confirmed coronavirus disease 2019 (COVID-19) diagnosed between March 1 and June 14, 2020. HCWs aged ≥18 years who worked during the exposure period and survived their illness were eligible for the survey. METHODS: After obtaining consent, 4,542 HCWs completed a standardized questionnaire. COVID-19 rates and proportions of exposures and practices were estimated and compared between ACHs and LTCFs. RESULTS: HCWs represented 13,726 (25%) of 54,005 reported COVID-19 cases in Québec and had an 11-times greater rate of COVID-19 than non-HCWs. Their secondary household attack rate was 30%. Most affected occupations were healthcare support workers, nurses and nurse assistants working in LTCFs (45%) and ACHs (30%). Compared to ACHs, HCWs in LTCFs had less training, higher staff mobility between working sites, similar PPE use, and better self-reported compliance with at-work physical distancing. Suboptimal IPC practices declined over time but were still present at the end of the first wave. CONCLUSION: Québec HCWs and their families were severely affected during the first wave of COVID-19. Insufficient pandemic preparedness and suboptimal IPC practices likely contributed to high transmission in both LTCFs and ACHs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".