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Record W4393988625 · doi:10.1177/08861099241237179

Perspectives of Male Service Providers on Responding to Intimate Partner Violence in Rural Zimbabwe: Findings From Qualitative Interviews

2024· article· en· W4393988625 on OpenAlexaff
Cyndirela Chadambuka, Ajwang’ Warria

Bibliographic record

VenueAffilia · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of CalgaryUniversity of Toronto
Fundersnot available
KeywordsQualitative researchDomestic violenceService providerIntimate partnerService (business)SociologyPsychologyNursingCriminologyGender studiesSuicide preventionMedicinePoison controlMedical emergencyBusinessSocial science

Abstract

fetched live from OpenAlex

Despite efforts made in Zimbabwe to address intimate partner violence (IPV), critical gaps still exist regarding the efficacy of IPV service provision in rural areas. This study examines male service providers’ perceptions of IPV in Chimanimani Rural District in Zimbabwe to provide guidance for policy and practice on IPV intervention in rural areas. A qualitative study was conducted with six male service providers using in-depth interviews and thematic analysis. Our findings revealed how the intersection of social norms and the geographical location of rural areas influence service providers’ perceptions of IPV and service provision. Most of the service providers interviewed had a general understanding of IPV and regarded it as unacceptable social behavior. However, constraints to service delivery typical in rural areas negatively impacted their IPV intervention, including having limited intervention resources, limited male participation, and conflicting identities (professional versus cultural identity). We conclude that service provision in rural areas is essential to respond to and prevent the occurrence of IPV, yet how service providers perceive IPV affects the quality of services rendered to victims.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.397
Teacher spread0.357 · 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 designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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