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Record W4280625057 · doi:10.2196/33184

Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples

2022· article· en· W4280625057 on OpenAlexvenueno aff
Sylvia Guendelman, Elizabeth Pleasants, Coye Cheshire, Ashley Kong

Bibliographic record

VenueJMIR Infodemiology · 2022
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsnot available
Fundersnot available
KeywordsAbortionThe InternetPopulationPopularityOnline searchIndex (typography)PillInformation retrievalComputer scienceInternet privacyWorld Wide WebMedicineFamily medicinePsychologyPregnancyNursing

Abstract

fetched live from OpenAlex

Background As access barriers to in-person abortion care increase due to legal restrictions and COVID-19–related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications. Objective We examined the extent to which people searched for out-of-clinic medication abortions in the United States in 2020 through 3 initial search terms: home abortion, self abortion, and buy abortion pill online. Methods Using the Google Trends website, we estimated the relative search index (RSI)—a comparative measure of search popularity—for each initial search term and determined trends and its peak value between January 1, 2020, and January 1, 2021. RSI scores also helped to identify the 10 states where these searches were most popular. We developed a master list of top search queries for each of the initial search terms using the Google Trends application programming interface (API). We estimated the relative search volume (RSV)—the search volume of each query relative to other associated terms—for each of the top queries using the Google Health Trends API. We calculated average RSIs and RSVs from multiple samples to account for low-frequency data. Using the Custom Search API, we determined the top webpages presented to people searching for each of the initial search terms, contextualizing the information found when searching them on Google. Results Searches for home abortion had average RSIs that were 3 times higher than self abortion and almost 4 times higher than buy abortion pill online. Interest in home abortion peaked in November 2020, during the third pandemic wave, at a time when providers could dispense medication abortion using telemedicine and by mail. Home abortion was most frequently queried by searching for Planned Parenthood, abortion pill, and abortion clinic, presumably denoting varying degrees of clinical support. Consistently lower search popularity for self abortion and buy abortion pill online reflect less population interest in mostly or completely self-managed out-of-clinic abortions. We observed the highest interest for home abortion and self abortion in states hostile to abortion, suggesting that state restrictions encourage these online searches. Top webpages provided limited evidence-based clinical content on self-management of abortions, and several antiabortion sites presented health-related disinformation. Conclusions During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. While our study was mainly descriptive, showing how infrequent abortion-related search data can be analyzed through multiple resampling, future studies should explore correlations between the keywords denoting interest in out-of-clinic abortion and abortion care measures and test models that allow for improved monitoring and surveillance of abortion concerns in our rapidly evolving policy context.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.342
GPT teacher head0.408
Teacher spread0.066 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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