The Effects of Downloading a Government-Issued COVID-19 Contact Tracing App on Psychological Distress During the Pandemic Among Employed Adults: Prospective Study
Bibliographic record
Abstract
BACKGROUND: Downloading a COVID-19 contact tracing app may be effective in reducing users' worry about COVID-19 and psychological distress. OBJECTIVE: This 2.5-month prospective study aimed to investigate the association of downloading a COVID-19 contact tracing app, the COVID-19 Contact Confirming Application (COCOA), released by the Japanese government, with worry about COVID-19 and psychological distress in a sample of employed adults in Japan. METHODS: A total of 996 full-time employed respondents to an online survey conducted May 22-26, 2020 (baseline), were invited to participate in a follow-up survey August 7-12, 2020 (follow-up). A high level of worrying about COVID-19 and high psychological distress were defined by baseline and follow-up scores on a single-item scale and the Kessler 6 (K6) scale, respectively. The app was released between the two surveys, on June 17. Participants were asked at follow-up if they downloaded the app. RESULTS: A total of 902 (90.6%) of 996 baseline participants responded to the follow-up survey. Among them, 184 (20.4%) reported that they downloaded the app. Downloading of the contact tracing app was significantly negatively associated with psychological distress at follow-up after controlling for baseline variables, but not with worry about COVID-19. CONCLUSIONS: This study provides the first evidence that using a government-issued COVID-19 contact tracing app may be beneficial for the mental health of employed adults during the COVID-19 pandemic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".