Investigating SARS-CoV-2 infection and the health and psychosocial impact of the COVID-19 pandemic in the Canadian CHILD Cohort: study methodology and cohort profile
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic is affecting all Canadian families, with some impacted differently than others. Our study aims to: 1) determine the prevalence and transmission of SARS-CoV-2 infection among Canadian families, 2) identify predictors of infection susceptibility and severity of SARS-CoV-2 and 3) identify health and psychosocial impacts of the COVID-19 pandemic. Methods This study builds upon the CHILD Cohort Study, an ongoing multi-ethnic general population prospective cohort consisting of 3454 Canadian families with children born in Vancouver, Edmonton, Manitoba, and Toronto between 2009-12. During the pandemic, 1462 CHILD households (5378 individuals) consented to participate in the CHILD COVID-19 Add-On Study involving: (1) brief biweekly surveys about COVID-19 symptoms and testing; (2) quarterly questionnaires assessing COVID-19 exposure, testing and vaccination status, physical and mental health, and pandemic-driven life changes; (3) in-home biological sampling kits to collect blood and stool. Mean ages were 9 years (range 0-17) for children and 43 years (range 18-85) for adults. Prevalence of SARS-CoV-2 infection will be estimated from survey data and confirmed through serology testing. We will combine these new data with a wealth of pre-pandemic CHILD data and use multivariate modelling and machine learning methods to identify risk and resilience factors for susceptibility and severity to the direct and indirect effects of the pandemic. Interpretation Our short-term findings will inform key stakeholders and knowledge users to shape current and future pandemic responses. Additionally, this study provides a unique resource to study the long-term impacts of the pandemic as the CHILD Cohort Study continues.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".