Changes in digital healthcare search behavior during the early months of the COVID-19 pandemic: A study of six English-speaking countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Public interest is an important component influencing the likelihood of successfully implementing digital healthcare. The onset of the COVID-19 pandemic allowed us to assess how public interest in digital health changed in response to disruptions in traditional health services. In this study, we used a difference-in-differences approach to determine how digital healthcare search behavior shifted during the early months of the COVID-19 pandemic compared to the same period in 2019 across six English-speaking countries: the United States, Canada, the United Kingdom, New Zealand, Australia, and Ireland. In most cases, we observed that the official declaration of the COVID-19 pandemic on 11 March 2020 was associated with a significant overall increase in the volume of digital healthcare searches. We also found notable heterogeneity between countries in terms of the keywords that were used to search for digital healthcare, which could be explained by linguistic differences across countries or the different national digital health landscapes. Since online searches could be an initial step in the pathway to accessing health services, future studies should investigate under what circumstances increased public interest translates into demand for and utilization of digital healthcare.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it