“It Was a Mistake, but We Knew That Something Might Happen”: Narratives of Teenage Girls' Experiences With Unintended Teenage Pregnancy
Bibliographic record
Abstract
It has been over a quarter of a century since the sexual reproductive health of young people came under the spotlight. The upsurge in human immunodeficiency virus (HIV) infections spurred on an era of intense development of services and strategies to ensure people's reproductive health rights were attainable, including the right to choose when to fall pregnant and have a baby. The statistics on teenage pregnancy are more than just numbers, but a represent stark reality for some girls in South African schools. Given that pregnancy in the teenage years is largely unintentional, prevention strategies need to extend beyond addressing risky sexual behavior; gaining deeper insights into teenagers' experiences and the events leading up to pregnancy would serve to better inform pregnancy prevention programs. This study explored the experiences of teenage mothers and pregnant teenagers, with the objective of acquiring a broader understanding of alternative approaches to preventing unintended pregnancy. A qualitative study was conducted in Ekurhuleni's township in the east of Johannesburg, South Africa. Fifteen narrative interviews with girls aged 13-19 years were conducted between July 2015 and July 2016, and were analyzed chronologically through narrative analysis. The findings revealed that participants who had engaged in socio-sexual and romantic relationships had no intention of falling pregnant and were familiar with existing strategies to prevent pregnancy. Social-sexual relationships were presented as an important aspect of their lives and demonstrated their ability to create spaces and opportunities to spend time with their social sexual partners and engage in sexual activity. Focusing on how teenage girls evaluate their sexual activity against the consequences of their actions is critical. However, sexual and reproductive health programs should refrain from representing young people's sexual behavior as a pathological condition, framing it instead as an integral component of creative sexual development. Programs should include relevant practical advice in relation to sexual engagement and be considered an extension of the State's existing Road to Health program.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".