Delays in access to care for abortion-related complications: the experience of women in Northeast Brazil
Bibliographic record
Abstract
Around 18 million unsafe abortions occur in low and middle-income countries and are associated with numerous adverse consequences to women’s health. The time taken by women with complications to reach facilities where they can receive appropriate post-abortion care can influence the risk of death and the extent of further complications. All women aged 18+ admitted for abortion complications to public-sector hospitals in three capital cities in the Northeastern Brazil between August-December 2010 were interviewed; medical records were extracted (N = 2,804). Nearly all women (94%) went straight to a health facility, mainly to a hospital (76.6%); the rest had various care-seeking paths, with a quarter visiting 3+ hospitals. Women waited 10 hours on average before deciding to seek care. 29% reported difficulties in starting to seek care, including facing challenges in organizing childcare, a companion or transport (17%) and fear/stigma (11%); a few did not initially recognize they needed care (0.4%). The median time taken to arrive at the ultimate facility was 36 hours. Over a quarter of women reported experiencing difficulties being admitted to a hospital, including long waits (15%), only being attended after pregnant women (8.9%) and waiting for a bed (7.4%). Almost all women (90%) arrived in good condition, but those with longer delays were more likely to have (mild or severe) complications. In Brazil, where access to induced abortion is restricted, women face numerous difficulties receiving post-abortion care, which contribute to delay and influence the severity of post-abortion complications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".