Findings and lessons learned from developing a 5-year community-based intervention for preventing early marriage in rural Gambia
Bibliographic record
Abstract
BACKGROUND: Preventing Early Marriage in Rural Gambia: Testing an Intervention was a 5-year project that aimed to address early marriage among girls in 53 rural communities in The Gambia. At baseline, the aim of the project was to identify the social and cultural factors that contribute to early marriage for girls aged 10-19. The baseline findings revealed that factors such as ethnicity and parents' concerns about their daughters engaging in premarital sex were significant contributors to early marriage for girls. Additionally, the lack of viable alternatives to marriage was also identified as a key factor. This information was utilized by the project team to design and implement the project intervention that included community engagement forums and discussion sessions and capacity building for key community stakeholders. METHODS: This study compared the project's baseline and endline data to assess the impact of the project intervention on girls' age at first marriage and changes in parents' knowledge of and attitudes toward early marriage and its prevention. It utilized a non-experimental evaluation design. RESULTS: The study results showed a significant increase in the average age of girls at first marriage, from 15.9 at baseline to 23.9 years at endline (P < 0.0001). Additionally, parents who actively participated in the community engagement forums and discussions have significantly improved their understanding of the harmful effects of early marriage on girls. This new knowledge has empowered these parents to re-evaluate the necessity of early marriage for girls. CONCLUSION: A key lesson we learned from developing this project intervention is that locally-based interventions, carefully designed and implemented with meaningful participation from key community stakeholders, have the potential to address the underlying causes of early marriage for girls in rural communities in The Gambia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".