Wealth and unintended pregnancy in Ghana: Analysis of 2014 Ghana Demographic and Health Survey
Bibliographic record
Abstract
Abstract Background: Pregnancy intention is a critical factor for both short and long term maternal and 27 child health outcomes. Some evidence show that wealth status has varying implications on 28 unintended pregnancy. In this study, we investigated wealth and unintended pregnancy among 29 women of reproductive age in Ghana. 30Methods: Our descriptive analysis comprised calculation of wealth status and unintended 31 pregnancy. The same calculation was done for socio-demographic characteristics and 32 unintended pregnancy. Due to the binary nature of the outcome variable (unintended 33 pregnancy), Binary Logistic Model was used for the inferential analysis. The first model 34 (Model I), constituted wealth quintile and unintended pregnancy. The second model (Model II) 35 was developed by adjusting for five key socio-demographic variables. 36Results: Women in the richest wealth quintile had less likelihood of experiencing unintended 37 pregnancy (OR=0.740, CI=0.42-1.28). Considering women aged 15-19 as the reference 38 category, women in all other age categories had less likelihood of unintended pregnancy 39 especially those aged 45-49 (AOR=0.26, CI=0.04-1.58). The findings revealed that those who 40 listened to radio at least once a week (AOR=0.56, CI=0.36-0.89) were less probable to report 41 unintended pregnancy, having those not listening to radio at all as the reference category. 42 Women in urban settings were less likely to have unintended pregnancies (AOR=0.74, 43 CI=0.46-1.19). 44Conclusions: This study has indicated that unintended pregnancy to larger extent is poverty 45 driven. The study suggests that the mass media, particularly radio, is valuable in 46 communicating birth control measures and messages on unintended pregnancies. Efforts to 47 halt unintended pregnancies must target poor women, especially those in the rural locations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".