Fostering the wellbeing of adolescent girls through an out-of-school sexuality education programme in Ghana
Bibliographic record
Abstract
Ghanaian adolescents face considerable sexual and reproductive health challenges that can disrupt human capital formation. However, a positive and healthy transition from adolescence to adulthood can be achieved by increasing access to sexual and reproductive health information, education, and services. This paper documents emerging findings from an adolescent girls’ programme delivered in 56 districts of Ghana. Data were collected from girls and young women aged 10–24 years who participated in the programme as part of a larger study that included in-school and out-of-school participants. In-depth interviews (IDI) [N = 49] and focus-group discussions (FGDs) [N = 13] were conducted in November 2021 and November 2022. An abductive approach was used to develop key themes from the data. The analysis revealed that the programme showed promise in changing the attitudes of beneficiaries towards safe sexual practices through condom use, in increasing assertiveness towards sexual and gender-based violence, and in real-life application of the knowledge and skills gained for health and social wellbeing. Out-of-school sexuality education programmes such as the one described here can be important catalysts for developing and maintaining meaningful relationships with others through the provision of information and education, and service delivery.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".