Cellular Immunity and Seroprevalence of Antibodies against SARS-CoV-2: Characterisation of Three Populations of Food Workers [CISACOV, study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background: Food industry employees were deemed essential workers as they provided crucial services during the pandemic. They were perceived at greater risk of infection than those working remotely. Aims of the CITF-funded study: The CISACOV study aimed to measure the COVID-19 infection rates of Quebec food industry workers and hardware workers, and to identify risk factors for this infection during the first years of the pandemic. Additionally, the study aimed to evaluate the immune response to SARS-CoV-2 infection and/or vaccine from blood samples collected during the study. Methods: This cohort enrolled participants from multiple service sectors (grocery stores, restaurants/ bars and hardware store workers) in the greater Quebec City area and Chaudière-Appalaches region. Serological testing at enrolment was used to identify previous infection, and follow-up samples were collected every 12 weeks for up to 5 visits, tracking both infection and vaccination. Summary of the contributed data: The datasets provides characteristics on 304 participants, covering demographics (age, sex, workplace region, weekly working hours), general health (BMI, smoke, other diseases, flu vaccine), exposure risk factors (household, travel history, gathering), longitudinal follow-up for COVID infections (COVID test, symptoms), SARS-CoV-2 vaccination and serology. In addition, a total of 1,299 serology samples, one to five per participant, were collected between April 2021 and October 2022.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".