Prevalence and Predictors of Contraceptives Use among Women Aged (15–49 years) with Induced Abortion History in Ghana
Bibliographic record
Abstract
Background. The incidence of abortion in Ghana ranges from 27 per 1000 to 61 per 1000 women, causing gynecological complications and maternal mortality. The use of modern contraceptives and its associated factors among women aged 15–49 years have been documented. However, utilization of modern contraceptives specifically among women with induced abortion history is underreported. This study therefore aimed at determining the proportion and identifying predictors of contraceptives use in this underreported population. Methods. This study used secondary data from the 2017 Ghana Maternal Health Survey (GMHS) for the analysis. The analysis is on a weighted sample of 3,039 women aged (15–49 years) with a history of induced abortion. Both descriptive and inferential methods were employed. The chi-square test, univariate and multivariate logistic regression techniques were used to assess statistical associations between the outcome variable and the predictors. Statistical significance was set at 95% confidence interval and p values ≤0.05. Results. Out of the 3,039 participants, 37% (95% CI: 34.6, 38.84) used contraceptives. We identified women’ age, union, place of residence, knowledge of fertile period, total pregnancy outcomes, and region as strong significant (95% CI, p≤0.05 ) predictors of post induced abortion contraceptives use. Conclusion. Contraceptives use among this vulnerable population is low. Therefore, there is a need to provide widespread access to postabortion contraception services and enhance efforts to efficiently integrate safe abortion practices law into health services in Ghana.
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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 | 0.000 | 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 | 0.000 |
| 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".