Structural violence: An evolutionary concept analysis
Bibliographic record
Abstract
AIMS: To enhance conceptual clarity and interdisciplinary understanding of structural violence, and to illuminate its implications for contemporary social justice and health equity research, by: (1) synthesizing scholarly literature pertaining to structural violence and health; (2) defining its key attributes, antecedents, consequences and characteristics; (3) contextually situating this phenomenon over time and across disciplines. DESIGN: A comprehensive review of scholarly health literature pertaining to 'structural violence' or its surrogate term 'structural determinants of health' was guided by a Research and Education Librarian. DATA SOURCES: In November 2019 and again in April 2021, CINAHL, Embase, Global Health, Medline, PsycINFO, PubMed and Scopus electronic databases were searched for peer-reviewed articles that described structural violence in the context of health. Of the 238 unique records identified, 32 articles were selected for inclusion and comprise the review sample. REVIEW METHODS: Using Beth Rodgers' evolutionary concept analysis method, articles were comparatively analysed to identify key attributes, antecedents and consequences associated with the concept's use in health research. RESULTS: The five interrelated attributes characterizing structural violence are: power, marginality, oppression, adversity and trauma. Hegemonic social, cultural, economic and political systems serve as antecedents, whilst the consequences of structural violence can be broadly classified as health inequity, injustice and indignity, and social disorganization. CONCLUSION: This analysis contributes to conceptual clarity and mutual understanding of the usage, application and significance of structural violence across health disciplines and provides a strong foundation for continued concept development and operationalization. Further research is needed to substantiate the relationship between structural violence and health inequity.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".