Women Traditional Psychosocial Coping Mechanisms Against Domestic Violence in Zimbabwe
Bibliographic record
Abstract
In spite of the numerous efforts to reduce domestic violence, including a robust legal and constitutional framework, the phenomenon remains high especially among women in rural Zimbabwe. This study examined the reasons why women in rural settings in Mashonaland Central are not willing to utilize the various legal and constitutional instruments for their own protection. The study explored the traditional psychosocial coping mechanisms for women against domestic violence in Mashonaland Central Province of Zimbabwe. The study relied on Galtung’s Conflict, Violence and Peace theory as the theoretical framework. The research was qualitative and employed a case study research design. Snowball sampling and purposive sampling were used to identify survivors of domestic violence and key informants. The study revealed that women are employing traditional psychosocial coping mechanisms such as silence, family support systems, religious belief systems and endurance to cope with violence. These practices are grounded in cultural and social practices purportedly aimed at preserving the family institution. The study concludes that these cultural practices have undermined the good intentions of the legal and constitutional frameworks that are in place to fight domestic violence and women abuse and recommends an approach that tries to deal with these strong cultural beliefs to make the laws effective.
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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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".