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Record W6969460239 · doi:10.5683/sp3/i9a7ry

The Canadian COVID-19 Cohort Study for people working in healthcare [CCCS study data contributed to the CITF Databank]

2024· dataset· en· W6969460239 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Practices
Canadian institutionsSinai Health System
Fundersnot available
KeywordsCohort studyHealth careAsymptomaticCohortVaccinationIncidence (geometry)ReceiptRisk assessmentYoung adult

Abstract

fetched live from OpenAlex

Background: 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. Aims of the CITF-funded study: 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. Methods: 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. Contributed dataset contents: 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.003

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.

Opus teacher head0.096
GPT teacher head0.340
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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