A Social-Ecological Model to Explore Multi-Faceted Drivers of Child Marriage: An Iterative Qualitative Study in Southern Bangladesh
Bibliographic record
Abstract
Despite national priorities, legal reforms, and increased investment in interventions, child marriage (CM) remains a significant public health risk, leading to violence, intergenerational nutritional depletion, and poor health outcomes in Bangladesh. Using the social-ecological model (SEM), this iterative qualitative study aimed to understand the drivers of CM at the individual, familial, social/community, and institutional levels to inform policy and programs. A total of 29 focus group discussions (with community members, married and unmarried adolescent girls, and their parents and grandmothers), 44 in-depth interviews (with married and unmarried adolescent girls, and their parents), and 10 key informants' interviews (influential community leaders) were conducted. Findings were drawn through thematic analysis employing both inductive and deductive coding. Identified CM drivers are aligned with the SEM framework. Girls' agency, collective efficacy, self-initiated marriage, and educational performance were individual-level drivers. Family-associated drivers were household poverty, parents' lack of awareness, and intra-household gendered preferences. Social/community drivers include norms about the "ideal" bride, girls' readiness for marriage, control over girls' sexuality and mobility, fear of violence, family honor, and religious norms. Weak enforcement to prevent CM, limited opportunities for girls, ecological conditions, and long school closures during COVID-19 were key institutional drivers. Findings suggest CM drivers are interconnected across levels of the SEM, implying the need for multi-level interventions. Coordinated efforts to reduce CM may include addressing the harmful CM norms and systemic factors leading to CM, raising community awareness about the adverse outcomes of CM, and offering poverty alleviation and economic opportunities for girls.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".