The Canadian COVID-19 Cohort Study for people working in healthcare [CCCS study data contributed to the CITF Databank]
Bibliographic record
Abstract
<b>Background:</b> There is a lack of data on the rates of infection with SARS-CoV-2 and risk factors for infection in healthcare workers (HCW), who are at high risk of exposure. This knowledge is important to mitigate risk factors and protect their communities in- and out-side the workplace by improving protective guidelines. <br> <b>Aims of the CITF-funded study:</b> This study aimed to identify risk factors for contracting SARS-CoV-2 in the workplace, household, and personal environments to assess the variance in risk due to exposure. The objectives were 1) to determine the incidence of symptomatic and asymptomatic infection; 2) study the use and effectiveness of vaccines in HCWs; 3) determine the pattern of immune responses over time via serology; and 4) to measure the mental health impact of working during a pandemic. <br> <b>Methods:</b> This cohort study enrolled full and part-time HCWs between the ages of 18 and 75 in Alberta, Nova Scotia, Ontario, and Quebec recruited via internal institutional advertisements and social media. A serum or dried blood spot sample was collected at enrolment, 30 days after receipt of each COVID-19 vaccine and each positive PCR or RAT, and every six months thereafter to assess IgG antibody levels. Participants completed questionnaires at enrolment regarding risk factors, vaccinations, past infections. Questionnaires were every 10 weeks to collect exposure data and as needed to collect vaccination and illness information. <br> <b>Contributed dataset contents:</b> The datasets include 2164 participants who completed baseline questionnaires between June 2020 and March 2022. 87% of participants gave one or more blood samples for SARS-CoV-2 serology over this period. Variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province, household, education, occupation), general health (smokes; asthma, lung disease, or other chronic disease diagnosis; height and weight; flu vaccine), SARS-CoV-2 vaccination, and serology.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".