What motivates individuals to share information with governments when adopting health technologies during the COVID-19 pandemic?
Bibliographic record
Abstract
BACKGROUND: While digital governance has been adopted by governments around the world to assist in the management of the COVID-19 pandemic, the effectiveness of its implementation relies on the collection and use of personal information. This study examines the willingness of individuals to engage in information-sharing with governments when adopting health technologies during the COVID-19 pandemic. METHODS: Data were obtained from a cross-sectional survey of 4,800 individuals drawn from 16 cities in China in 2021. Tobit regression models were used to assess the impacts of an array of determinants on an individual's willingness to share information with governments when adopting health technologies. RESULTS: Individuals who perceived a higher level of helpfulness, risk, expectations from others, weariness toward privacy issues, and were sensitive to positive outcomes were more willing to share information with governments when adopting health technologies during the COVID-19 pandemic. Across all the subgroups, self-efficacy only reduced the willingness to share information with governments for individuals who spent more than seven hours per day online. The negative impacts of being sensitive to negative outcomes on the willingness to share information were only found among females and the less educated group. CONCLUSIONS: This study revealed the seemingly paradoxical behavior of individuals who perceived high risks of sharing information and a sense of fatigue toward privacy issues yet continued to be willing to share their information with their governments when adopting health technologies during the COVID-19 pandemic. This work highlighted significant differential motivations for sharing information with governments when using health technologies during a pandemic. Tailored policies that resonate with population sub-groups were suggested to be proposed to facilitate crisis management in future situations.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".