Contraception and abortion in times of crisis: results from an online survey of Venezuelan women
Bibliographic record
Abstract
Introduction: In the last decade, Venezuela has experienced a complex humanitarian crisis that has limited access to healthcare. We set out to describe Venezuelan women's experiences accessing sexual and reproductive health services, including abortion, which is heavily restricted by law. Methods: We fielded an online survey in July of 2020 among Venezuelan women recruited through social media advertisements. We conducted descriptive statistical analyses using Excel and STATA SE Version 16.0. Results: We received 851 completed survey responses. Almost all respondents experienced significant hardship in the last year, including inflation (99%), worries about personal safety (86%), power outages (76%), and lack of access to clean water (74%) and medications (74%). Two thirds of respondents used contraception in the last two years, and almost half (44%) of respondents had difficulty accessing contraception during that same time period. About one fifth of respondents reported having had an abortion; of these, 63% used abortion pills, and 72% reported difficulties in the process. Half of those who had an abortion did it on their own, while the other half sought help - either from family members or friends (34%), from providers in the private health sector (14%), or from the Internet (12%). Conclusions: Venezuelan women who responded to our survey describe a harsh context with limited access to sexual and reproductive health services. However, they report relatively high rates of contraceptive use, and abortion seems to be common despite the restrictive legal setting.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".