Prevalence and predictors of contraceptives use among women age (15-49) with induced abortion history in Ghana
Bibliographic record
Abstract
Abstract Background Abortion incidence in Ghana ranges from 27 per 1000 to 61 per 1000 women, causing major gynecological complications or problems and maternal mortality. Though, the use of modern contraceptives has been documented to be a reliable public health preventive measure towards reducing unwanted pregnancies, only 19% of women aged (15-49) with abortion history receive post-abortion contraception support. This study therefore aimed at determining the proportion and identifying predictors of contraceptives use in these underreported and vulnerable 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 history of induced abortion. Both descriptive and inferential methods were employed. 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 3039 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<0.005) predictors of post induced abortion contraceptives use.Conclusion Contraceptives use among this vulnerable population is low. Therefore, there is the need to provide widespread access to post-abortion contraception services and enhance efforts to efficiently integrate the 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 | 0.000 | 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) | 0.003 | 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".