COVID-19 Occupational Risks, Seroprevalence and Immunity among Paramedics in Canada [study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background: Due to the nature of their job, paramedics are at high risk for COVID-19 infection and transmission as they perform various procedures and treatments that may increase their exposure to the virus. Aims of the CITF funded study: CORSIP-Canada aimed to determine past infection prevalence and risk of infection by SARS-CoV-2 among paramedics by measuring infection-induced and vaccine-acquired antibodies, and vaccination. It also aimed to investigate their attitudes towards vaccines and risk factors in the workplace to establish optimal strategies and safety guidelines for protection. Methods: Paramedics above the age of 19 in British Columbia, Ontario, Saskatchewan, Alberta, and Manitoba were individually recruited into a cohort study via institutional promotion and external advertisements. Participants submitted a baseline questionnaire and blood sample and completed follow-up questionnaires and blood samples every 6 months for one year. Contributed dataset contents: The datasets include 3709 participants who completed baseline questionnaires between January 2021 and March 2023. 76% of participants gave one or more dried blood spots or blood samples for SARS-CoV-2 serology between Jan 2021 and Feb 2023. About 1600 participants provided longitudinal data with serology results for up to 2 years with a median follow-up time of 11 months (median 3 samples). Approximately 380 additional participants gave dried blood spots or blood samples for SARS-CoV-2 serology without completing a questionnaire. Questionnaire variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province, education, household composition, occupations), general health (tobacco use; chronic conditions; height and weight; flu vaccine), COVID infection test results, symptoms and exposure risks, SARS-CoV-2 vaccination.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".