Is “Movember” an Effective Prostate Cancer Awareness Campaign Beyond the English Language? Insights From Google Trends Among Spanish Speakers
Bibliographic record
Abstract
Objective To evaluate the impact of the “Movember” awareness campaign (men’s health campaign that takes place every November) on internet search trends for information online about prostate cancer and to compare the results with those for “Pinktober” (the breast cancer awareness campaign that takes place in October) in the Spanish language as an indirect measure of its effectiveness. Methods Google Trends was used to evaluate the monthly relative search volumes (RSV) of the terms “cáncer de próstata” (prostate cancer), “cáncer de mama” (breast cancer), and “Movember” from January 2009 to December 2019 both in Spain and worldwide (in the Spanish language). Breast cancer was used as a comparator of the campaign impact. Mean increase in RSV on-campaign and off-campaign was calculated and compared using the Mann-Whitney U test and Joinpoint regression analysis to assess loss or gain of interest. Results The term “cáncer de próstata” showed a statistically significant increase during the campaign months both in Spain (17.4%; P < 0.001) and worldwide (35.4%; P < 0.001). Both “cáncer de próstata” and Movember showed a decreasing trend worldwide and in Spain, while “cáncer de mama” showed an increasing trend. Conclusion The Movember campaign generates a statistically significant increase in the search trends on “cancer de próstata” (prostate cancer) during the month of November (both in Spain and worldwide); when compared with the breast cancer campaign “Pinktober” these increases are of a lesser magnitude but still significant, suggesting that the campaign is effective beyond the English language, although the interest has been decreasing throughout the years.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".