What Canadians search about asthma: A 10-year Google Trends study on asthma and related topics
Bibliographic record
Abstract
Google Trends (GTr) is now becoming an important tool in epidemiological studies to assess the trend of online searches by people of any geographical location. While previous studies assessed trends in searches for some allergic conditions, very few studies reported search patterns for asthma and asthma-related topics. We took monthly Canadian GTr data for asthma-related keywords such as ‘asthma’, ‘shortness of breath’, ‘wheeze’, ‘asthma emergency’, ‘asthma hospital’, and ‘asthma medication’ between January 1, 2014, and December 31, 2023. We assessed the trend of yearly relative search volume (RSV) for the past 10 years. We also assessed the trend by influenza and wildfire seasons. We observed a marginal increase in searches for some asthma-related topics such as ‘asthma’ ‘shortness of breath’, and ‘wheeze’ in Canada between 2014 to 2023 but not other topics such as ‘asthma emergency’, ‘asthma hospital’, and ‘asthma medication’. The pattern of searches for ‘asthma’, ‘shortness of breath’, and ‘wheeze’ did not change by influenza of wildfire seasons across the years. Our study is the first to demonstrate searches for asthma and related topics by Canadians in the past 10 years and indicates GTr as an emerging tool in epidemiology that can be used in planning the implementation of public health policies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".