Differential occupational risks to healthcare workers from SARS-CoV-2: A prospective observational study
Bibliographic record
Abstract
Abstract Background Personal protective equipment (PPE) and social distancing are designed to mitigate risk of occupational SARS-CoV-2 infection in hospitals. Why healthcare workers nevertheless remain at increased risk is uncertain. Methods We conducted voluntary Covid-19 testing programmes for symptomatic and asymptomatic staff at a UK teaching hospital using nasopharyngeal PCR testing and immunoassays for IgG antibodies. A positive result by either modality determined a composite outcome. Risk-factors for Covid-19 were investigated using multivariable logistic regression. Results 1083/9809(11.0%) staff had evidence of Covid-19 at some time and provided data on potential risk-factors. Staff with a confirmed household contact were at greatest risk (adjusted odds ratio [aOR] 4.63 [95%CI 3.30-6.50]). Higher rates of Covid-19 were seen in staff working in Covid-19-facing areas (21.2% vs. 8.2% elsewhere) (aOR 2.49 [2.00-3.12]). Controlling for Covid-19-facing status, risks were heterogenous across the hospital, with higher rates in acute medicine (1.50 [1.05-2.15]) and sporadic outbreaks in areas with few or no Covid-19 patients. Covid-19 intensive care unit (ICU) staff were relatively protected (0.46 [0.29-0.72]). Positive results were more likely in Black (1.61 [1.20-2.16]) and Asian (1.58 [1.34-1.86]) staff, independent of role or working location, and in porters and cleaners (1.93 [1.25-2.97]). Contact tracing around asymptomatic staff did not lead to enhanced case identification. 24% of staff/patients remained PCR-positive at ≥6 weeks post-diagnosis. Conclusions Increased Covid-19 risk was seen in acute medicine, among Black and Asian staff, and porters and cleaners. A bundle of PPE-related interventions protected staff in ICU.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".