GENDER NORMS AND ATTITUDE TOWARDS ABORTION AMONG VERY YOUNG ADOLESCENTS IN KENYA AND NIGERIA
Bibliographic record
Abstract
Abstract Introduction Unsafe abortion is a major cause of death in sub-Saharan African countries with very young adolescents (VYAs) at increased risk due to their high vulnerability to unprotected sex and unplanned pregnancies. Abortion beliefs and attitudes are considered to be partly rooted in traditional views on gender and religious influences. This study is informed by the limited data on gender norm perception and its association with abortion among VYAs despite the increasing prevalence of unsafe abortion reported among this group. Materials Data for this study was collected as part of a longitudinal survey on the gendered socialization and sexual and reproductive health of very young, in-school adolescents aged 10-14 years in Kenya and Nigeria. The study obtained quantitative data from 1,912 VYAs using a structured questionnaire. The results presented in this paper are from the quantitative baseline data collected in Kenya and Nigeria Result The study found significant regional differentials in attitudes toward abortion and gender norm perception of the VYAs from the two regions. VYAs from Nigeria were more likely to endorse abortion practices relative to their counterparts from Kenya. Factors associated with endorsement of abortion practice were gender norms about Sexual Double Standards (SDS) and Normative Heterosexual Relation (NHR) in Nigeria and knowledge of where to get a condom, NHR, and Normative Romantic Relationship (NRR) in Kenya. Conclusion Intervention efforts seeking to promote positive gender norms and attitudes towards SRH must begin with the VYAs and must consider regional variations and address knowledge and access to SRH commodities. What is already known on this topic previous studies have explored attitudes towards gender norms and abortion among young people and adults, with little known among very young adolescents aged 10-14 years. What this study adds the study contributed to the limited research on gender norms and attitudes towards abortion among very young adolescents. How this study might affect research, practice, and policy The outcome of this suggests the need to consider regional variations when developing interventions and policies addressing gender norms and attitudes towards abortion in Africa.
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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 | 0.001 |
| 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) | 0.002 | 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".