A multilevel analysis of contextual risk factors for intimate partner violence in Ghana
Bibliographic record
Abstract
While extant research suggests that context, structural socioeconomic and cultural factors matter in intimate partner violence research, quantitative research on the subject in sub-Saharan Africa, and in particular Ghana, has disproportionately focused on prevalence and individual level correlates of spousal violence. This research has ignored the role of the structural socioeconomic and cultural factors and contexts in understanding the causes and consequences of spousal violence in a setting where family life is heavily influenced by traditional norms and beliefs. These norms and beliefs may lead to inadequate and ineffective interventions geared at preventing or reducing spousal violence and its consequences. Guided by an integrated theoretical approach, this study addresses these issues by estimating a multilevel logistic regression model where the effects of both individual and community level risk factors for spousal violence are assessed. Data for the study come from the Ghana Demographic and Health Survey and the Ghana Population and Housing Census. The findings confirm the salient role of structural socioeconomic and cultural factors, such as patriarchal norms and residential instability, in the perpetration of spousal violence against women. Policy implications of these findings and directions for further research are discussed.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".