Qualitative Examination of the Role and Influence of Mothers-in-Law on Young Married Couples’ Family Planning in Rural Maharashtra, India
Bibliographic record
Abstract
Unmet need for family planning (FP) continues to be high in India, especially among young and newly married women. Mothers-in-law (MILs) often exert pressure on couples for fertility and control decision making and behaviors around fertility and FP, yet there is a paucity of literature to understand their perspectives. Ten focus group discussions (FGDs) were carried out with MILs of young married women (aged 18-29 years) participating in a couple-focused FP intervention as a part of a cluster-randomized intervention evaluation trial (the CHARM2 study) in rural Maharashtra, India. FGDs included questions on their roles, attitudes, and decision making around fertility and FP. Audio-recorded data were translated/transcribed into English and analyzed for key themes using a deductive coding method. MILs reported having social norms of early fertility and son preference. They understood that family size norms are lower among daughters-in-law and that spacing can be beneficial but were not supportive of short-term contraceptives, especially before the first child. They preferred female sterilization, opposed abortion, had apprehensions around side effects from contraceptive use, and had misconceptions about the intrauterine device, with particular concerns around its coercive insertion. MILs mostly believed that decision making should be done jointly by a husband and wife, but that as elders, they should be consulted and involved in the decision-making process. These findings highlight the need for engagement of MILs for FP promotion in rural India and the potential utility of social norms interventions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".