Measuring Unmet Need for Contraception Using a Person‐Centered Algorithm: An Application With a Community‐Based Sample of Married Rohingya Women in Bangladesh
Bibliographic record
Abstract
The standard measure of unmet need for contraception is not person-centered and may not adequately represent women's contraceptive needs. To demonstrate the strength of a modified measure, we replicated the standard algorithm for unmet need, then created a person-centered algorithm that considers (1) whether nonusers want to use contraception and (2) whether users want to use a different method. We applied the standard and person-centered algorithms to a sample of 847 married Rohingya women aged 15-49 years living in camps in Cox's Bazar, Bangladesh, a population about whom little is known regarding contraceptive need. Forty-six percent of respondents were currently using contraception. Among users, 14 percent wanted to use a different method and 36 percent of nonusers wanted to use a method. Using the standard algorithm, 39 percent had "unmet need," 18 percent had "no need," and 44 percent had "met need." Using the person-centered measure, 24 percent had "unmet need," 38 percent had "no need," and 38 percent had "met need." The standard algorithm may overestimate unmet need among Rohingya nonusers, and the person-centered measure provides evidence of method dissatisfaction among users. This measure also complements existing person-centered measures of need and is an example of how incremental change can improve our understanding of women's contraceptive needs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".