A Bother or a Benefit? How Contraceptive Users Balance the Trade-Offs Between Preferred Menstrual Bleeding Patterns and Preferred Contraceptive Methods in India, South Africa, and the United States
Bibliographic record
Abstract
The contraceptive needs of women for whom the benefits of menstrual bleeding may outweigh its disadvantages have largely been overlooked, especially outside high-income countries. Some providers and researchers have assumed that users of nonhormonal birth control (non-HBC) are misinformed about the positive and negative effects of HBC and/or the need for menstrual bleeding. This study takes the position that many of those rejecting HBC in favor of methods that do not alter bleeding are, in fact, making informed decisions. Using questionnaire data from 4,255 contraceptive users in three countries, we compared current HBC users explicitly open to hormone use (“H-receptive”) and non-HBC users explicitly rejecting hormones (“H-averse”). To the extent that menstrual bleeding attitudes affect contraceptive choice, these two groups should have the greatest contrasts in those attitudes. This novel study design mitigates ambiguities arising from posing hypothetical scenarios to those ambivalent about using hormones or who are not currently using contraception. In all three countries, of those agreeing with the prompt, “I don’t want to change my natural menstrual cycle,” the fractions of H-averse and H-receptive users are disproportionally high and low, respectively (p ≤ .0026). Responses to other prompts varied across populations, revealing complex juxtapositions of multiple criteria, including bleeding preferences, that likely influence contraceptive choices. These patterns, reflecting personal and culturally salient values, highlight the necessity of not assuming that menstrual bleeding is undesirable or relying on a single criterion to ascertain clients’ contraceptive needs and preferences. Rather, acknowledging a client’s personal hierarchy of preferences regarding contraceptive attributes best serves their goals.
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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 | 0.003 | 0.013 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".