Digital surveillance: The interest in mouthwash‐related information
Bibliographic record
Abstract
BACKGROUND: The comprehension of the interests of Internet users regarding their health-related searches may reveal the community's demands about oral health. The study aimed to characterize the interests of Google users related to mouthwash in Australia, Brazil, Chile, Japan, Mexico, Russia, the United Kingdom, the United States, Saudi Arabia and South Africa applying the Google Trends. METHODS: This longitudinal retrospective study analysed the mouthwash-related interest of Google users from January 2004 to December 2020. The monthly variation of relative search volume (RSV) and the main queries related were determined using Google Trends. Autoregressive integrated moving average (ARIMA) forecasting models were built to establish the predictive RSV values for mouthwash for additional 12 months. Auto-correlation plots and a general additive model (GAM) were used to diagnose trends and seasonality in RSV curves. In addition, the influence of social isolation related to the outbreak of COVID-19 were analysed. RESULTS: The RSVs curves showed a considerable increase in searches related to mouthwash to AUS, BRA, JAP, MEX, GBR and USA (RSV > 25), while the growth was slight to CHI, KSA, RSA and RUS (RSV < 25) over the years, without influence of monthly seasonality. All countries showed a significant increase in mouthwash interest after the outbreak of COVID-19, except for KSA and RUS. The mouthwash-related searches were associated to specific brands or chemical compositions, treatments, whitening agents, homemade mouthwash and indications for the 'best mouthwash'. CONCLUSIONS: In general, there was an increasing interest of Google users in mouthwash-related topics between 2004 and 2020. In addition, in most countries, there was an expansion in searches during the social isolation of the COVID-19 pandemics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".