Indigenous practitioners’ views on causes of female infertility
Bibliographic record
Abstract
Background: The use of indigenous practices has increased remarkably throughout the world. Subsequently, society uses this practice for the treatment of various health problems, including infertility. This research focussed on the role of indigenous practitioners (IPs) using a holistic approach to explore the causes of infertility in women. Aim: This study aimed to explore and describe the views of IPs on the causes of female infertility in Ngaka Modiri Molema health district. Setting: The study was conducted in Ngaka Modiri Molema, North West Province, one of the most rural provinces in South Africa. Methods: The study followed a qualitative explorative design. A purposive sampling technique identified five IPs who were experts in managing infertility. Individual semi-structured interviews were conducted, and data analysis used Creswell's method of qualitative data analysis. Results: Findings revealed that IPs offered a wide range of services in the treatment and management of infertility among rural women. Hence, the following themes emerged, namely, history taking regarding infertility, treatment of infertility and holistic care on infertility. Conclusion: The IPs are important providers of healthcare in the management of infertility in indigenous communities. The findings revealed that there are various causes of female infertility according to the indigenous healthcare system. Contribution: In contribution, the study described the unique practices found in the community as executed by the IPs. This care focusses on holistic care, including treatment and continuous care for the healthcare user and the family. Noteworthy to mention, this holistic care extends to subsequent pregnancies. However, there is a need for further research to valorise the indigenous knowledge unearthed in this study.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".