Determinants of young People’s gender norm attitudes related to rights and equity in Southeast Nigeria
Bibliographic record
Abstract
BACKGROUND: Gender norms shape rights, privileges, and societal roles, impacting health and well-being. In low- and middle-income countries (LMICs), while young people may reject harmful gender discrimination, they often support other norms that perpetuate gender imbalances. This study examines factors influencing young people's attitudes toward gender norms, focusing on men's rights in decision-making, education, social roles, and equity for women's empowerment in Southeast Nigeria. METHODS: A cross-sectional analytical study was conducted from October 2022 to March 2023 in six communities in Ebonyi State, Nigeria, with 1025 young people aged 15-24 selected using cluster sampling. Gender norm attitudes were assessed using a three-point Likert scale questionnaire. Mean scores exceeding 1.5 indicated favourability towards negative gender norms. Statistical analysis included univariate, bivariate, and multivariate linear regression with statistical significance set at a p < 0.05. RESULTS: Over 60% of participants believed that men should have more decision-making power than women, but fewer than 30% supported unequal access to education for boys. More than two-thirds endorsed women/girls' empowerment. Predictors of support for men's decision-making rights, education, and protective rights included schooling status (β = -1.896, p < 0.001), educational status (β = 5.593, p = 0.004), gender (β = 0.529, p < 0.001), mother's educational level (β = 1.993, p = 0.020), household leadership (β = 0.218, p = 0.003) and religion: β = -0.175, p = 0.008). Predictors of support for women's empowerment included gender (β = 10.299, p < 0.001), schooling status (β = -0.170, p = 0.012), and father's educational level (β = -0.212, p = 0.041). CONCLUSION: Despite widespread support for gender equality for women's empowerment, young people in the Southeast still favour traditional gender norms, particularly decision-making and educational rights for men. Factors such as educational status, gender, and household leadership play a significant role in shaping these attitudes. Interventions that promote gender equity should focus on education, challenge traditional norms, and engage both young men and women in discussions about shared decision-making and equal access to opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".