Maluma et al., 2017 Prevalence of Traditional Herbal Medicine use and associated factors among pregnant women of Lusaka Province, Zambia
Bibliographic record
Abstract
Background: Traditional herbal medicine (THM) use during pregnancy places women at high risk of adverse maternal health outcomes. Aim: This study determined the prevalence and factors associated with traditional herbal medicine use in pregnancy. Methods: A cross-sectional study was conducted using a structured interviewer-administered questionnaire for data collection. Two hundred and seventy-three women aged 18–45 years of Chongwe and Chawama communities of Lusaka province were identified for the study. Statistical analysis was done using SPSS v.20 (IBM SPSS Inc., Chicago, IL, USA). Proportions and frequencies were used to describe results and Pearson’s Chi-square test with continuity correction was used to determine association between categorical variables. Fisher’s Exact test was used where more than 20% of the expected frequency was less than 5. Cramer’s V test was used to determine strength of association.Results: More than a quarter (32%) of the study participants had used traditional herbal medicine at some point during pregnancy. Among the users of traditional herbal medicine, almost all (99%) used it to accelerate labour. Knowledge, socio-cultural beliefs and practices, including myths and misconceptions about pregnancy and delivery were factors associated with THM use. Conclusion: Women in Chongwe and Chawama communities in Lusaka province of Zambia prevalently used traditional herbal medicine in pregnancy. Concerted efforts, including health education interventions are needed to reduce this practice.
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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 | 0.001 |
| 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".