The relationship between sociodemographic factors and reporting having terminated a pregnancy among Ghanaian women: a population-based study
Bibliographic record
Abstract
Background: Pregnancy termination is an illegal medical procedure in Ghana and 88% of induced abortions are performed in unsafe conditions, thus recipients face an elevated risk of abortion-related complications. This study aims to explore the associations between sociodemographic factors and reporting having terminated a pregnancy among Ghanaian women. Methods: Logistic regression models were estimated using data from the 2014 Ghana Demographic and Health Survey (n=9396). ORs were computed for the associations between reporting pregnancy termination and select demographic and socio-economic factors. Results: Education level, employment status, financial status and marital status of women are significantly associated with reporting having terminated a pregnancy. Conclusions: Women who are employed, cohabit with a partner and are considered middle class or wealthy are more likely than their counterparts to report having terminated a pregnancy. Ghanaian women with intermediate levels of education are more likely than both their more- and less-educated counterparts to report having terminated a pregnancy. These findings highlight the need for the development of policies aimed at reducing unsafe abortions associated with unintended pregnancies. Specific recommendations include providing family planning education and outreach to high-risk groups to reduce unintended pregnancies and improving working conditions for expectant mothers, including provisions for paid maternity leave and job protection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".