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Record W4416717539 · doi:10.1186/s12889-025-24737-2

Factors associated with self-isolating and completing a contact follow-up program: a retrospective analysis of Ontario, Canada’s COVID-19 contact tracing initiative

2025· article· en· W4416717539 on OpenAlexaffabout
Justin Thielman, Celina Degano, Samantha Gray, Andrea Chambers, Elaina MacIntyre

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldComputer Science
TopicCOVID-19 Digital Contact Tracing
Canadian institutionsPublic Health Ontario
FundersJohns Hopkins University
KeywordsContact tracingBiostatisticsMetropolitan areaPublic healthTracingEpidemiologyMEDLINEHealth services research

Abstract

fetched live from OpenAlex

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.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.084
GPT teacher head0.325
Teacher spread0.241 · 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
GenreEmpirical

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
Published2025
Admission routes2
Has abstractyes

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