Factors associated with self-isolating and completing a contact follow-up program: a retrospective analysis of Ontario, Canada’s COVID-19 contact tracing initiative
Bibliographic record
Abstract
BACKGROUND: Contact tracing is an important tool in slowing the spread of infectious disease and preventing illness. People may face barriers to contact tracing adherence, depending on various personal and contextual characteristics. Thus, we examined a large-scale COVID-19 contact tracing initiative and compared adherence across several socio-demographic and exposure characteristics. METHODS: We analyzed data for 130,255 participants in Ontario's COVID-19 contact tracing initiative from September 2020 to December 2021. During contact follow-up calls, callers recorded whether contacts reported self-isolating and whether they completed follow-up. We performed unadjusted and adjusted logistic regressions to estimate the odds ratios (OR) and 95% confidence intervals (CI) of self-isolating and of completing follow-up, according to contacts' age group, neighbourhood material resources, COVID-19 wave, exposure setting, region, and preferred language. RESULTS: In the adjusted analyses, odds of completing follow-up decreased as neighbourhood-level material resources decreased, with OR = 0.57 (95% CI: 0.54-0.60) comparing highest resource areas to lowest. Compared to COVID-19 contacts living in the Greater Toronto Area, other Ontario regions had higher odds of completing follow-up, with ORs ranging from 1.19 (95% CI: 1.14-1.24) to OR = 1.91 (95% CI: 1.78-2.04). Contacts whose preferred languages were not English had lower odds of self-isolating (OR = 0.57, 95% CI: 0.38-0.84). The 0-4 and 5-11 year old age groups had lower odds of self-isolating than 20-29 year olds, with respective ORs of 0.60 (95% CI: 0.48-0.75) and 0.57 (95% CI: 0.48-0.67). CONCLUSIONS: In preparing for future pandemics, contact tracing programs may benefit from prioritizing additional supports for those who live in areas with fewer material resources, have language preferences other than English, and live in large metropolitan centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".