Assessment of the Effect of the Go Red for Women Campaign on Search Engine Queries for Cardiovascular Disease in Women
Bibliographic record
Abstract
BACKGROUND: The Go Red for Women (GoRedW) campaign aims to increase awareness of cardiovascular disease (CVD) and stroke in women. However, assessing the effects of social campaigns on information-seeking behaviors may be challenging. The purpose of this study was to ascertain the effect of GoRedW using a large sample of unbiased real-world data from Google Trends (GTr) and evaluate the temporal correlation of online search queries for CVD and stroke in women with GoRedW. METHODS: We conducted a retrospective study using GTr, a public tool from the Google search engine to obtain relative search volumes (RSVs) related to CVD and stroke in women in the period January 2004 to April 2019 in the USA. In addition, trends of GoRedW were compared with those of the well-established Breast Cancer Awareness Month (BCAM) campaign. RESULTS: RSVs increased for queries of GoRedW and all searched terms for CVD but not for stroke in women during February compared to other months of the year without active campaign. The strong pattern with peaks of temporal increase was consistent over the 15-year study period. RSV of "Go Red for Women" in February increased on average 494% (range: 211% to 789%). The highest temporal increase on search queries for CVD was for the term "heart disease (in) women" with an average of 114% (24% to 182%). We found a positive correlation between RSVs of GoRedW and the term "heart disease (in) women" (r = 0.54, P < 0.001). RSVs for "Breast Cancer Awareness Month" had a higher increase during the active campaign month compared to GoRedW and showed a stronger positive correlation (r = 0.78, P < 0.001). CONCLUSIONS: Search engines are a valuable resource to provide insights on information demand and to assess the effectiveness of social campaigns and interventions. Our study showed an increase in the RSVs for queries of GoRedW and all CVD terms which correlated with the active campaign months over a 15-year period.
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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 | 0.022 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".