Potential risk factors associated with COVID-19 in health care workers
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: 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.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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