The Effects of Downloading a Government-Issued COVID-19 Contact Tracing App on Psychological Distress During the Pandemic Among Employed Adults: Prospective Study
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it