Violence by Burning Against Women and Girls: An Integrative Review
Bibliographic record
Abstract
Violence against women and girls by burning is a serious and confronting form of gender-based violence. Often, perpetrators aim to disfigure their victims or cause great pain, rather than kill them. Little is known about the characteristics of females who are subjected to violence by burning. This study aimed to review the literature concerning the prevalence, demographic profile, injury event, contributing factors and health outcomes for women and girls who have experienced burn-related violence. A search across five databases (PubMed, CINAHL, PsycINFO, Scopus and LILACS) was conducted up to April 2021 to identify original peer-review research, with a focus on violence by burning against women and girls. The review was guided by the five-stage approach to integrative reviews developed by Whittemore and Knafl (2005). Fifteen studies were identified. Victims were predominantly married, with low socio-economic status, limited education, and high emotional and financial dependency on their partners or families. Burn injuries were mostly caused by flame or acid, with significant morbidity or high mortality. Motives included family/marital issues or property/financial disputes. This review identified the limited evidence available in the peer-reviewed literature related to burn-related violence against women and girls worldwide. Findings suggest the need for further research to provide a clearer understanding of the complex issues involved.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".