What Guidance Do Violence Journals Provide for Reporting on Sexual and Gender Diversity? A Document Analysis
Bibliographic record
Abstract
BACKGROUND: Gender-based violence is a worldwide health and social problem with negative short- and long-term health impacts. Sexual and gender minority people experience more gender-based violence and significant barriers to support. These populations are often not included in, or are actively excluded from, gender-based violence research, and sexual orientation and gender are generally poorly measured and reported. One recommendation put forth to improve the evidence base with regard to sexual and gender diversity is higher standards of research and reporting by academic journals. Given the leading role of nurse researchers in this area of women's health, this is a topic of particular importance for nursing research and education. AIMS: We examined the sexual and gender diversity-related guidance provided by academic violence journals to authors, editors and peer-reviewers. METHODS: We conducted a descriptive document analysis. Two researchers independently searched for, and coded, guidance related to sexual and gender diversity from 16 websites of academic journals focused on violence research. RESULTS: While most journals included some mention of diversity or inclusion, only about half provided in-depth guidance for authors, editors and/or peer-reviewers. Guidance related to gender was more common than guidance for sexual diversity. The journals gave varied prominence to diversity-related guidance, and it was often difficult to locate. CONCLUSIONS: To reflect the spectrum of lived experiences of gender and sexuality, publishers must actively direct authors, editors and reviewers to include, measure and report these experiences. This has not yet been fully achieved in the important area of gender-based violence research, and is of direct concern to nurse researchers who contribute significantly to this body of knowledge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".