Menstrual regulation, unsafe abortion, and maternal health in Bangladesh.
Bibliographic record
Abstract
Maternal mortality has declined considerably in Bangladesh over the past few decades. Some of that decline--though precisely how much cannot be quantified--is likely attributable to the country's menstrual regulation program,which allows women to establish nonpregnancy safely after a missed period and thus avoid recourse to unsafe abortion. Key Points. (1) Unsafe clandestine abortion persists in Bangladesh. In 2010, some 231,000 led to complications that were treated at health facilities, but another 341,000 cases were not. In all, 572,000 unsafe procedures led to complications that year. (2) Recourse to unsafe abortion can be avoided by use of the safe, government sanctioned service of menstrual regulation (MR)--establishing nonpregnancy after a missed period, most often using manual vacuum aspiration. In 2010, an estimated 653,000 women obtained MRs, a rate of 18 per 1,000 women of reproductive age. (3) The rate at which MRs result in complications that are treated in facilities is one-third that of the complications of induced abortions--120 per 1,000 MRs vs. 357 per 1,000 induced abortions. (4) There is room for improvement in MR service provision, however. In 2010, 43% of the facilities that could potentially offer it did not. Moreover, one-third of rural primary health care facilities did not provide the service. These are staffed by Family Welfare Visitors, recognized to be the backbone of the MR program. In addition, one-quarter of all MR clients were denied the procedure. (5) To assure that trends toward lower abortion-related morbidity and mortality continue, women need expanded access to the means of averting unsafe abortion. To that end, the government needs to address barriers to widespread, safe MR services, including women's limited knowledge of their availability, the reasons why facilities do not provide MRs or reject women who seek one, and the often poor quality of care.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".