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Record W4389567169 · doi:10.5539/gjhs.v16n1p1

“Creating a Generation of Equality”: A Stakeholder’s Perspective on Power Dynamics and Gender-Based Violence in Zimbabwe

2023· article· en· W4389567169 on OpenAlexvenueno aff
Thulani Runyararo Dzinamarira, Miriam Mutevere, Stephen Nyoka, Enos Moyo, Lorcadia Muzenda, Fortunate Kakumura, Tafadzwa Dzinamarira

Bibliographic record

VenueGlobal Journal of Health Science · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentTransformative learningWomen's empowermentFocus groupDomestic violenceQualitative researchPsychological interventionSociologyStakeholderPhotovoicePower (physics)Poison controlGender studiesSuicide preventionPublic relationsEconomic growthPolitical scienceMedicineNursingSocial scienceEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Background: Gender-based violence (GBV) remains a significant public health concern in Zimbabwe, with 38% of women experiencing intimate partner violence. Rooted in the country's patriarchal structure, power imbalances contribute to this epidemic. This study aimed to investigate the perspectives of key stakeholders on the relationship between power dynamics and GBV in Zimbabwe, and as well as to explore potential interventions to address this issue. Methodology: A descriptive qualitative research design was used. We collected data from fourteen participants using three focus group discussions. Qualitative content analysis was used to analyze the data. Results: Three themes that emerged on power dynamics and GBV were economic inequality, gender stereotypes, and lack of access to justice. Two themes that emerged for recommendations were gender-transformative and economic empowerment programming. Conclusion: Findings from this study underscore the need to include men in designing and implementing gender-transformative programs alongside economic empowerment initiatives to effectively address GBV and dismantle patriarchal structures in Zimbabwe.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.182
GPT teacher head0.446
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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