Sociodemographic Determinants of Adolescent Childbirths in Ghana: Results from a nationwide survey
Bibliographic record
Abstract
Abstract Objective: To investigate the sociodemographic determinants of adolescent childbearing in Ghana.Methods: This research studied reproductive-aged women who were systematically sampled in Ghana. Kaplan Meier and Cox proportional hazard regression with shared frailty were fitted on a nationwide population-based data from the 2014 Ghana Demographic and Health Survey (GDHS) to predict adolescent childbirth.Results: The analysis revealed that approximately 49% initiated childbirth during adolescence. This study found higher adolescent childbirth rates among women who were not working (adjusted hazard ratio (AHR) =1.17; 95% confidence interval (CI) = 1.07 to 1.29) and resided in rural areas (AHR =1.22; 95% CI = 1.09 to 1.37). In addition, poor women (AHR= 1.71; 95% CI = 1.49 to 1.95) and middle-class women (AHR=1.67; 95% CI = 1.48 to 1.88) had a higher likelihood of having adolescent birth compared to the rich. Lastly, secondary and higher educated women were 0.74 (95% CI= 0.67 to 0.82) and 0.10 (95% CI= 0.06 to 0.16) times less likely to have adolescent birth, respectively.Conclusions: This research revealed that adolescent childbearing was greatly influenced by socioeconomically disadvantaged background within the contexts of the women and the community in which they reside. This finding suggests the need to initiate and improve interventions that seek to reduce poverty among women and to promote and encourage adolescent girls to pursue secondary and higher education to help reduce unintended adolescent childbirths. Finally, adolescent girls should be empowered through counseling and education on reproductive and sexual health options and rights to ensure more informed decision-making about contraception.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".