Naïve, uninformed and sexually abused: circumstances surrounding adolescent pregnancies in Malawi
Bibliographic record
Abstract
BACKGROUND: Pregnancy and childbearing in adolescence could negatively affect girls' health and socio-economic wellbeing across the life course. Previous studies on drivers of adolescent pregnancy in Africa have not fully considered the perspectives of parents/guardians vis-à-vis pregnant and parenting adolescents. Our study addresses this gap by examining pregnant and parenting adolescents' and parents/guardians' narratives about factors associated with early and unintended pregnancy. METHODOLOGY: The descriptive study draws on qualitative data collected as part of a larger mixed-methods cross-sectional survey on the lived experiences of pregnant and parenting adolescents. Data were collected between March and May 2021 in Blantyre, Malawi, using semi-structured interview guides. We interviewed 18 pregnant and parenting adolescent girls, 10 parenting adolescent boys, and 16 parents/guardians of pregnant and parenting adolescents. Recorded interviews were transcribed verbatim into the English language by bilingual transcribers. We used the inductive-thematic analytical approach to summarize the data. FINDINGS: The data revealed several interconnected and structural reasons for adolescents' vulnerability to early and unintended pregnancy. These include adolescents' limited knowledge and access to contraceptives, poverty, sexual violence, school dropout, COVID-19 school closures, and being young and naively engaging in unprotected sex. While some parents agreed that poverty and school dropout or COVID-19 related school closure could lead to early pregnancies, most considered stubbornness, failure to adhere to abstinence advice and peer influence as responsible for adolescent pregnancies. CONCLUSION: Our findings contribute to the evidence on the continued vulnerability of girls to unintended pregnancy. It highlights how parents and adolescents hold different views on reasons for early and unintended pregnancy, and documents how divergent views between girls and their parents may contribute to the lack of progress in reducing adolescent childbearing. Based on these findings, preventing unintended pregnancies will require altering community attitudes about young people's use of contraceptives and engaging parents, education sector, civil society organizations and community and religious leaders to develop comprehensive sexuality education programs to empower in- and out-of school adolescents.
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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 | 0.005 | 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 | 0.001 |
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".