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Record W4382010876 · doi:10.1177/10778012231182408

“The Little Money I Get Is Used to Buy Drugs”: A Qualitative Exploration of the Economic Cost of Intimate Partner Violence for Female Survivors in Ghana

2023· article· en· W4382010876 on OpenAlexafffund
Gervin Ane Apatinga, Eric Y. Tenkorang

Bibliographic record

VenueViolence Against Women · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMemorial University of NewfoundlandUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAbsenteeismDomestic violenceProductivityEconomic costIndirect costsPaymentPoison controlWork (physics)Occupational safety and healthInjury preventionScholarshipSuicide preventionHuman factors and ergonomicsQualitative researchMedicineBusinessEnvironmental healthEconomic growthPsychologyEconomicsSocial psychologySociologyFinanceEngineering

Abstract

fetched live from OpenAlex

Empirical research confirms the economic costs of intimate partner violence (IPV) for women. Yet, scholarship on this topic is lacking in Ghana, where IPV against women is commonplace. We used in-depth interviews with 15 female survivors of IPV in the Eastern Region to examine the economic costs of IPV for women. Findings showed that the economic costs were both direct and indirect. Direct costs included out-of-pocket payments for medical and nonmedical services, while indirect costs included diminished work abilities, increased absenteeism from work, and lowered work productivity. Ghanaian policymakers must enforce and strengthen policies to prevent violence against women.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.062
GPT teacher head0.377
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2023
Admission routes2
Has abstractyes

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