Tracking online searches for emotional wellbeing concerns and coping strategies in the UK during the COVID-19 pandemic: a Google Trends analysis
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic is the largest acute public health emergency of this century. Government intervention to contain the virus focuses on non-pharmacological approaches such as physical distancing/lockdown (stay-at-home orders). As the situation develops, the impact of these measures on mental health and coping strategies in individuals and the population is unknown. Methods: We used Google Trends data (01 Jan 2020 to 09 Jun 2020) to explore the changing pattern of public concern in the UK to government measures as indexed by changes in search frequency for topics related to mental distress as well as coping and resilience. We explored the changes of specific topics in relation to key dates during the pandemic. In addition, we examined terms whose search frequency increased most. Results: Following lockdown, public concerns - as indexed by relative search trends - were directly related to COVID-19 and practicalities such as ‘furlough’ (paid leave scheme for people in employment) in response to the pandemic. Over time, searches with the most substantial growth were no longer directly or indirectly related to COVID-19. In contrast to relatively stable rates of searches related to mental distress, the topics that demonstrated a sustained increase were those associated with coping and resilience such as exercise and learning new skills. Conclusions: Google Trends is an expansive dataset which enables the investigation of population-level search activity as a proxy for public concerns. It has potential to enable policy makers to respond in real time to promote adaptive behaviours and deliver appropriate support.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".