Unmet need for contraception and its association with unintended pregnancy in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Unmet need for contraception and unintended pregnancy are important public health concerns both in developing and developed countries. Previous researches have attempted to study the factors that influence unintended pregnancy. However, the association between unmet need for contraception and unwanted pregnancy is not studied adequately. The aim of the present study was to measure the prevalence of unmet need for contraception and unwanted pregnancy, and to explore the association between these two in a nationally representative sample in Bangladesh. METHODS: Data for the present study were collected from Bangladesh demographic and health survey conducted in 2011. Participants were 7338 mothers ageing between 13 and 49 years selected from both rural and urban residencies. Planning status of last pregnancy was the main outcome variable and unmet need for contraception was the explanatory variable of primary interest. Cross tabulation, chi-square tests and logistic regression (Generalised estimating equations) methods were used for data analysis. RESULTS: Mean age of the sample population was 25.6 years (SD 6.4). Prevalence of unmet need for contraception was 13.5%, and about 30% of the women described their last pregnancy as unintended. In the adjusted model, the odds of unintended pregnancy were about 16 fold among women who reported facing unmet need for contraception compared to those who did not (95% CI = 11.63-23.79). CONCLUSION: National rates of unintended pregnancy and of unmet need for contraception remain considerably high and warrant increased policy attention. Findings suggests that programs targeting to reduce unmet need for contraception could contribute to a lower rate of unintended pregnancy in Bangladesh. More in-depth and qualitative studies on the underlying sociocultural causes of unmet need can help develop context specific solutions to unintended pregnancies.
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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 | 0.000 | 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 it