Blood, men and tears: keeping IUDs in place in Bangladesh
Bibliographic record
Abstract
The Intra-Uterine Device (IUD) is an effective method of contraception, but in Bangladesh is associated with high levels of discontinuation within the first year. This study involved data collection from a retrospective cohort of women who had an IUD inserted 12 months earlier. In the cohort, 330 women were interviewed to identify factors associated with discontinuation. Later, 20 women, of the 103 who reported discontinuing because of excessive menstrual bleeding, were interviewed again and in depth about these issues. Of 330 women who had an IUD inserted, 47.3% had discontinued use one year post-insertion. In univariate and multivariate analyses, IUD discontinuation was strongly associated with side-effects (heavier periods; abdominal pain) and spousal factors (not discussing IUD with husband pre-insertion), but not with service delivery factors. In-depth interviews with women who reported excessive blood loss as the main reason for discontinuation found a doubling of both menstrual days and blood loss after IUD insertion. In Bangladesh, women cannot pray, have sexual intercourse, perform household tasks or participate in community activities during menstruation. Thus, women with menstrual side-effects faced serious physical, social and psychological challenges that made continuation difficult. Among those who discontinued, spouses were generally unsupportive and sometimes abusive, particularly when not involved in the decision to use the IUD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".