Role of maternal and child health services on the uptake of contraceptive use in India: A reproductive calendar approach
Bibliographic record
Abstract
BACKGROUND: According to the latest round of National Family Health Survey-4 (NFHS (2015-16)) maternal and child health care (MCH) services improved drastically compared to NFHS-3. Previous studies have established that the uptake of MCH services increases the likelihood of early adoption of contraceptives among women. So, our study aims to examine if the early initiation of contraceptive has proportionately improved with the recent increase in MCH services. METHODS: This study used the reproductive calendar of NFHS-4, 2015-16, to evaluate contraceptive initiation within 12 months after the last birth among 1,36,962 currently married women in India. A complementary log-log regression model was created to examine the link between the time of initiation of contraception and MCH care at the national level. RESULTS: It was found that only a quarter of women within 12 months from last birth have adopted the modern contraceptive method. Among those majority of the females adopted sterilization mostly at the time of birth. The multivariable model identified, that the period of initiation of contraceptive depends on the gender composition of children and access to MCH services. It was found that the odds of early initiation of contraceptive use was higher when a women have only son (AOR = 1.15,95% CI- 1.22, 1.18) compared to women with only daughter. Also, it was found that women who have availed MCH services were more likely to adopt contraceptives earlier. CONCLUSION: The number of women availing MCH services has increased in India, but it did not result in a proportional increase in initiation of contraception after childbirth. Facilitating family planning services alongside MCH services will be beneficial in low-resource settings. It is a golden opportunity to educate and encourage women for early adoption of contraceptive.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".