Do restrictive gender attitudes and norms influence physical and mental health during very young Adolescence? Evidence from Bangladesh and Ethiopia
Bibliographic record
Abstract
Adolescence is seen as a window of opportunity for intervention but also as a time during which restrictive gender attitudes and norms become more salient. This increasingly gendered world has the potential to profoundly influence adolescents' capabilities, including their physical and mental health. Using quantitative data on 6,500 young adolescents (10-12) from the Gender and Adolescence: Global Evidence (GAGE) program, this paper analyses the association between restrictive gender attitudes (RGAs) at the individual level and restrictive gender norms (RGNs) at the community level and physical and mental health in Bangladesh and Ethiopia. We find significant associations between RGAs and RGNs and height-for-age z-scores, body mass index z-scores, self-reported health, adolescent hunger, psychological well-being, and self-esteem. We find no relationship between RGAs or RGNs and illness. We also find heterogeneity across country and urbanicity. We find surprisingly limited variation by gender, and the differences we do see point to important vulnerabilities for both boys and girls. Our results point to the powerful role that distal factors such as culture and beliefs, as manifested through RGAs and RGNs, can play in shaping health outcomes for both boys and girls and suggest important next steps for future research and policy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".