Google and Women’s Health-Related Issues: What Does the Search Engine Data Reveal?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Identifying the gaps in public knowledge of women's health related issues has always been difficult. With the increasing number of Internet users in the United States, we sought to use the Internet as a tool to help us identify such gaps and to estimate women's most prevalent health concerns by examining commonly searched health-related keywords in Google search engine. METHODS: We collected a large pool of possible search keywords from two independent practicing obstetrician/gynecologists and classified them into five main categories (obstetrics, gynecology, infertility, urogynecology/menopause and oncology), and measured the monthly average search volume within the United States for each keyword with all its possible combinations using Google AdWords tool. RESULTS: We found that pregnancy related keywords were less frequently searched in general compared to other categories with an average of 145,400 hits per month for the top twenty keywords. Among the most common pregnancy-related keywords was "pregnancy and sex' while pregnancy-related diseases were uncommonly searched. HPV alone was searched 305,400 times per month. Of the cancers affecting women, breast cancer was the most commonly searched with an average of 247,190 times per month, followed by cervical cancer then ovarian cancer. CONCLUSION: The commonly searched keywords are often issues that are not discussed in our daily practice as well as in public health messages. The search volume is relatively related to disease prevalence with the exception of ovarian cancer which could signify a public fear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it