Has the pandemic enhanced and sustained digital health-seeking behaviour? A big data interrupted time-series analysis of Google Trends
Bibliographic record
Abstract
Abstract Background Due to the emergency responses early in the pandemic, the use of digital health in healthcare increased abruptly, yet it remains unclear whether this introduction was sustainable on the long term. We explore trends in digital health-seeking behaviour as proxy for readiness to adopt digital health as a mainstream form of healthcare. Methods We use weekly Google Trends data from February 2019 to August 2021 in Canada, United States, United Kingdom, New Zealand, Australia, and Ireland. We used five keywords to monitor online search interests in Google Trends: online doctor, telehealth, online health, telemedicine , and health app . Data are analysed using an interrupted time-series analysis with break-points on 11 March 2020 and 20 December 2020. Results Digital health searches immediately increased in all countries after the pandemic announcement. There was some variance in what keywords were used per country. However, searches declined after this immediate spike, sometimes towards pre-pandemic levels. The exception is the search volume of health app , which showed to either remain stable or gradually increase during the pandemic. Interpretation Our findings suggest that digital health-seeking behavioural patterns associated with the pandemic are currently not sustainable. Further building of digital health capacity and development of robust digital governance and literacy frameworks remain crucial to more structurally facilitate digital health transformation across countries. Funding Not applicable.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".