Potential risk factors associated with COVID-19 in health care workers
Bibliographic record
Abstract
BACKGROUND: Health care workers (HCWs) have been recognized as being at higher risk for coronavirus disease 2019 (COVID-19) infection; however, relevant factors and magnitude have not been clearly elucidated. AIM: This study was aimed to describe COVID-19 infections among hospital employees at a large tertiary care hospital located in Ontario, Canada from March to July 2020, towards better understanding potential risk factors. METHODS: Data on all HCWs with either a positive COVID test or a high-risk exposure from March to July 2020 were analyzed. HCWs with positive COVID test results and high-risk exposures were described. Those who developed COVID-19 following high-risk exposure were compared to those who did not. Data were also analyzed to determine trends over time. RESULTS: Over the period of observation, 193 staff (2% of total working staff) had a positive COVID-19 test. Incidence of HCW infections closely followed community incidence. Overall, 31% of COVID-19 cases were deemed occupationally acquired. Of these, 41% were acquired from a patient, with the remainder (59%) from fellow staff. Over the same period, 204 staff were identified as having a high-risk exposure. The majority of exposures (55%) were patient-associated, with the remaining (45%) resulting from staff-to-staff contact. Overall, 13% went on to develop COVID-19. Of these cases, 58% were patient-associated and 42% were a result of staff-to-staff transmission. CONCLUSIONS: HCWs are at risk for work-related COVID-19. Given the number of infections attributed to staff-staff transmission, greater attention could be paid to implementing prevention measures in non-clinical areas.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".