Retrospective review of Google Trends to gauge the popularity of global surgery worldwide: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Global surgery is a growing movement worldwide, but its expansion has not been quantified. Google Search is the most popular search engine worldwide, and Google Trends analyzes its queries to determine popularity trends. We used Google Trends to analyze the regional and temporal popularity of global surgery (GS). Furthermore, we compared GS with global health (GH) to understand if the two were correlated. METHODS: This is a retrospective cross-sectional study examining Google Trends of GS and GH. We searched the terms "global surgery" and "global health" on Google Trends (Google Inc., CA, USA) from January 2004 to May 2021. We identified time trends and compared the two search terms using SPSS v26 (IBM, WA, USA) to run summary descriptive analyses and Wilcoxon rank-sum tests. RESULTS: The ten countries most interested in GS were India (5.0%), the United Kingdom (5.0%), Ireland (4.0%), the United States (4.0%), Australia (3.0%), Canada (3.0%), New Zealand (3.0%), Germany (2.0%), South Africa (2.0%), and Nigeria (1.0%). GS became more popular after 2015 (2.3% vs. 1.3%, P < 0.001) and was consistently less popular than GH (1.6% vs. 45.3%, P = 0.04). The difference between GS and GH interest levels increased after 2015 (45.4% vs. 42.9%, P = 0.04). CONCLUSION: GS is less popular than GH, more popular in high-income countries, and has become more popular after 2015 when the Lancet Commission on Global Surgery published its seminal report. The World Health Organization passed resolution WHA 68.15. Future advocacy efforts should target low- and middle-income countries primarily.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".