Experience obtaining legal abortion in Uruguay: knowledge, attitudes, and stigma among abortion clients
Bibliographic record
Abstract
BACKGROUND: The abortion law in Uruguay changed in 2012 to allow first trimester abortion on request. Implementation of the law in Uruguay has been lauded, but barriers to care, including abortion stigma, remain. This study aimed to assess women's experiences seeking abortion services and related attitudes and knowledge following implementation of the law in Uruguay. METHODS: We interviewed 207 eligible women seeking abortion services at a high-volume public hospital in Montevideo in 2014. We generated univariate frequencies to describe women's experiences in care. We conducted regression analysis to examine variations in experiences of stigma by women's age and number of abortions. RESULTS: Most of the women felt that abortion was a right, were satisfied with the services they received, and agreed with the abortion law. However, 70% found the five-day waiting period unnecessary. Women experienced greater self-judgement than worries about being judged by others. Younger women in the sample (ages 18-21) reported being more worried about judgment than women 22 years or older (1.02 vs. 0.71 on the ILAS sub-scale). One quarter of participants reported feeling judged while obtaining services. Women with more than one abortion had nearly three times the odds of reporting feeling judged. CONCLUSIONS: These findings highlight the need to address abortion stigma even after the law is changed. Some considerations from Uruguay that may be relevant to other jurisdictions reforming abortion laws include: the need for strategies to reduce judgmental behavior from staff and clinicians towards women seeking abortions, including training in counseling skills and empathic communication; addressing stigmatizing attitudes about abortion through community outreach or communications campaigns; mitigating the potential stigma that may be perpetuated through policies to prevent "repeat" abortions; ensuring that younger women and those with more than one abortion feel welcome and are not mistreated during care; and assessing the necessity of a waiting period. The rapid implementation of legal, voluntary abortion services in Uruguay can serve in many ways as an exemplar, and these findings may inform the process of abortion law reform in other countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".