Does Higher Percentages of Women With Higher Education Within District Impacts Individuals Use of Contraception in Uttar Pradesh?
Bibliographic record
Abstract
Uttar Pradesh in India is high fertility state which contributes maximum to India’s population growth. The use of family planning method is amongst the lowest in the State and has witnessed a decline during the two consecutive National Family Health Survey (NFHS) period of round 3 & 4. The use of any methods of contraception declined from 56.3% in 2005-06 to 53.5 % in 2015-16. A decline of 2.8 percent points in-spite of all the programmatic push. Similarly, the use of any modern contraceptive methods declined from 48.5% to 47.8% during this period. This decline in the use of contraception necessitates revisiting determinants of contraceptive use at the district (group) level. The availability of district level data from NFHS-4 makes it possible to estimate between district variations in contraceptive use in UP. The Intra-class correlation coefficient of 0.1528 reveals that 15.28 percent of the variation in contraceptive use is due to between district differences in Uttar Pradesh while 84.72 percent variation is due to within district individual differences. At individual level younger age, higher parity, Hindu religion, educated secondary or higher school levels and those belonging to higher SES other than poor quintile have significant higher odds of contraceptive use. At district (group) level, the higher percentages of women educated higher school levels within district significantly determines the use of contraception in Uttar Pradesh.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.001 |
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".