Spousal Separation and Use of and Unmet Need for Contraception in Nepal: Results Based on a 2016 Survey
Bibliographic record
Abstract
Nepal is facing a large-scale labour migration—both internal and international—driven by economic and employment opportunities. There is sparse literature available at the national level which examines the link between migration and contraceptive use. This study aimed at identifying contraceptive use and the unmet need for family planning (FP) and exploring its correlates among the married women of reproductive age (MWRA) by their husbands’ residence status, using data from Nepal Demographic Health Survey 2016–a nationally representative cross-sectional survey. A stratified two-stage cluster sampling in rural and a three-stage sampling in urban areas were used to select the sampling clusters, and data from 11,040 households were analyzed. Reported values were weighted by sample weights to provide national-level estimates. The adjusted odds ratio (aOR) was calculated using multiple logistic regressions using complex survey design, considering clusters, and stratification by ecological zones. All analyses were performed using Stata 15.0. Among the total MWRA, 53% were using a contraceptive method, whereas the proportion of contraceptive use among the cohabiting couple was 68%. The unmet need for contraceptive use was 10% among cohabiting couples and 50% among the noncohabiting couples. Contraceptive use was significantly low among the women reporting an induced abortion in the last five years and whose husbands were currently away. A strong negative association of spousal separation with contraceptive use was observed (aOR:0.14; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>) after controlling other covariates, whereas a positive association was observed with the unmet need (aOR:8.00; <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math>). Cohabiting couples had a significantly higher contraceptive use and lower unmet need compared with the couples living apart. Between 2006 and 2016, contraceptive use increased by 1% per year among cohabiting couples, although this increase is hugely attributable to the use of traditional methods, compared with modern methods. The labour migration being a significant and indispensable socioeconomic phenomenon for Nepal, it is necessary to monitor fertility patterns and contraceptive use by cohabitation status in order to ensure that the national family planning interventions are targeted to address the contraceptive and fertility needs of the migrant couples.
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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 | 0.002 | 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".