Substance and alcohol use in pregnant women attending antenatal care at a tertiary hospital in Johannesburg, South Africa
Bibliographic record
Abstract
Background: Substance and alcohol use during pregnancy confers significant risk to the mother and foetus. Substance and alcohol use is common in South African general population. However, there is a paucity of literature on the extent of the problem among pregnant women. Aim: This study assessed the prevalence of substance use and its predictors among pregnant women attending antenatal care (ANC) at a tertiary hospital in Johannesburg, South Africa. Setting: This study was conducted at Rahima Moosa hospital, Johannesburg. Methods: This study was a retrospective record review of 399 consecutively selected pregnant women attending ANC. Socio-demographic, clinical, and substance use data were extracted and analysed using descriptive statistics and multivariate analyses. Results: Most pregnant women (84%) were aged between 20 years and 40 years. Substance use was documented in 45% (N = 178) of the records. Of these, concurrent use of alcohol and tobacco was 63% (n = 113). Factors that predicted the use of substances in pregnancy were low birth weight (aOR = 2.5, 95% CI = 1.23, 5.16, p = 0.01) and a positive HIV status (aOR = 0.6. 95% CI = 0.35, 0.96, p = 0.04). Conclusion: There was a high prevalence of substance use among pregnant women in the context of this study. Contribution: The increased risk of contracting HIV and having babies with low birth weights when substances are used in pregnancy highlights the need for appropriate behaviour modification for these women during antenatal care and this is in line with the health belief model.
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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 | 0.001 | 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".