Perceptions of users and non-users of an early contact tracing mobile application to fight COVID-19 spread: a value-based empirical investigation
Bibliographic record
Abstract
Purpose The purpose of this study is to describe a comparative study of the perceptions of users and non-users of an early contact tracing application helping to prevent the spread of the COVID-19 pandemic. The unprecedented incidence of this disease warrants investigating theoretically the use of mobile contact tracing applications as a promising approach to curtail its transmission. Design/methodology/approach A consumption value-based model of the adoption and use of a contact tracing mobile application was built and tested through a cross-sectional survey conducted with 2 samples (of 309 already users and 306 non-users) in the Province of Alberta, Canada. Findings Utilitarian and social values together with health information seeking and perceived critical mass drive the use of the application while perceived privacy risk is an obstacle to usage for both users and non-users. Research limitations/implications Study participants self-assessed their risk category of potential exposure to the COVID-19 virus, and this was a subjective measure including an emotional component. Practical implications No major differences in the approaches targeting users and non-users of a mobile contact tracing application to encourage its adoption and use are necessary. Social implications Additional efforts are required to convey to people information on the benefits and current rate of use of such an application and to mitigate privacy risk concerns. Originality/value Overall, the study offers theoretical and practical contributions that may help improve the adoption and usage of contact tracing applications addressing the COVID-19 pandemic or other possible public health crises.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".