Understanding the rise in traditional contraceptive methods use in Uttar Pradesh, India
Bibliographic record
Abstract
BACKGROUND: The sustainable development goals (SDG) aim at satisfying three-fourths of family planning needs through modern contraceptive methods by 2030. However, the traditional methods (TM) of family planning use are on the rise, along with modern contraception in Uttar Pradesh (UP), the most populous Indian state. This study attempts to explore the dynamics of rising TM use in the state. METHODS: We used a state representative cross-sectional survey conducted among 12,200 Currently Married Women (CMW) aged 15-49 years during December 2020-February 2021 in UP. Using a multistage sampling technique, 508 primary sampling units (PSU) were selected. These PSU were ASHA areas in rural settings and Census Enumeration Blocks in urban settings. About 27 households from each PSU were randomly selected. All the eligible women within the selected households were interviewed. The survey also included the nearest public health facilities to understand the availability of family planning methods. Univariate and bivariate analyses were conducted. Appropriate sampling weights were applied. RESULTS: Overall, 33.9% of CMW were using any modern methods and 23.7% any TM (Rhythm and withdrawal) at the time of survey. The results show that while the modern method use has increased by 2.2 percentage points, the TM use increased by 9.9 percentage points compared to NFHS-4 (2015-16). The use of TM was almost same across women of different socio-demographic characteristics. Of 2921 current TM users, 80.7% started with TM and 78.3% expressed to continue with the same in future. No side effects (56.9%), easy to use (41.7%) and no cost incurred (38.0%) were the main reasons for the continuation of TM. TM use increased despite a significant increase (66.1 to 81.3%) in the availability of modern reversible methods and consistent availability of limiting methods (84.0%) in the nearest public health facilities. CONCLUSION: Initial contraceptive method was found to have significant implications for current contraceptive method choice and future preferences. Program should reach young and zero-parity women with modern method choices by leveraging front-line workers in rural UP. Community and facility platforms can also be engaged in providing modern method choices to women of other parities to increase modern contraceptive use further to achieve the SDG 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".