Exploring User-Uptake of Digital Contact Tracing Apps - A Practitioner Guide - Special Edition/Module 10 - Case Study: Canada
Bibliographic record
Abstract
This module aims to explore Digital Contact Tracing (D-CT) developed and implemented in Canada for the COVID-19 response. Focus is on the country’s D-CT app, COVID Alert, and understanding user-uptake. The case study begins with a brief overview of the country’s overall response to COVID-19 and the impact of the virus on the country. Following, we explain Canada’s app by describing, how it emerged, how it is designed and functions, how users engage with the app across the whole user-engagement process, and what user-uptake looks like in the country. The next section examines all eight factors identified in the other case studies (1) Perceptions of Data Collection & Management; 2) Sense of Community; and 3) Communications & Misinformation; 4) Accessibility & Inclusion; 5) Trust in Public/Private Institutions; 6) Policy & Governance; 7) Response Infrastructure; and 8) Digital Capability) plus a ninth factor specific to Canada — Provincial & Territorial Support.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".