Prevalence and Determinants of Self-Medication Among Pregnant Women at an Antenatal Clinic at Soba Teaching Hospital, Sudan: A Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND Rates of self-medication among pregnant women are high, due to the promotion of herbal and dietary supplements and lack of awareness of possible adverse effects. This study evaluated self-medication in pregnant women attending an antenatal clinic at Soba Teaching Hospital, Sudan. MATERIAL AND METHODS A quantitative study was conducted using a pre-tested semi-structured interviewer-administered questionnaire, which consisted of 25 questions divided into 4 sections: demographic and obstetric; self-medication source, recommendations, and conditions; most commonly used medications and herbal medicine; reasons for self-medications. A total of 230 pregnant women were included in the study. The chi-square test was used to test associations between variables and the binary logistic regression model was used to evaluate the relationship between self-medication practice and explanatory variables. A P value of <0.05 was deemed significant in the final model. RESULTS Of the 230 pregnant women interviewed, 67% were multigravida, 184 (80%) practiced self-medication, 45.6% used pharmaceutical products, commonly analgesics (32.5%), and 21.9% used herbal remedies, including peppermint (19.4%) and citrus fruits (17.5%). Self-medication was used for nausea (49.5%) and heartburn (46.2%). Reasons for self-medication included belief in safety (40%) and the expense of physician fees (28.1%). Socio-demographic characteristics and the prevalence of self-medication in pregnant women showed no significant associations. CONCLUSIONS The findings from this study showed that the prevalence of self-medication reported by pregnant women attending antenatal clinics in Sudan was high and included approved drugs and herbal medicines, mainly from pharmacies, and was driven by the perception that all medications supplied by pharmacies were safe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".