Prevalent use of herbs for reduction of labour duration in Mwanza, Tanzania: are obstetricians aware?
Bibliographic record
Abstract
Background: The use of herbs during pregnancy and labour is rapidly increasing because the herbs are considered to be natural and therefore free of risks. Despite of this perception, a number of herbs have been reported to have negative effects to the new-borns and the mothers. Therefore, this study aimed to determine the prevalence and factors associated with the use of herbs during labour among women in Mwanza, Tanzania.Methods: The study involved women who delivered at Bugando Medical Centre and Sekou Toure Hospital in Mwanza, north-western Tanzania. Data were collected using questionnaires. Comparison of prevalence of herb use by various factors was done. Results: A total of 178 women were involved in the study. The mean age of participants was 26.6 ± 5.4 years. The prevalence of herb use was found to be 23.0%. The use of herbs was significantly associated with marital status (p = 0.011) and the use during previous deliveries (p = 0.000).Conclusion: The study findings signify that, about a quarter of women in Mwanza use herbs during childbirth and the use encourages recurrent use of these herbs in subsequent pregnancies. A large scale survey is recommended to determine the extent of use of traditional herbs during pregnancy and childbirth countrywide. Studies to determine the toxic profile of the herbs which are used are also needed so as to address the matter to the community.
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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) | 0.002 | 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".