Intersectionality in surgical care in LMICs: A systematic scoping review
Bibliographic record
Abstract
BACKGROUND: This review examines the application of intersectionality in surgical care within low and middle-income countries (LMICs). Intersectionality is an analytic lens that examines how overlapping social identities intersect within structural systems of power and inequality. In the context of health, it helps explain how these intersections shape people's exposure to risk, access to care, and overall outcomes. Applying this lens can help uncover cumulative disadvantages and inequities within surgical systems in LMICs. METHODS: Following PRISMA guidelines, we conducted a comprehensive search across eight databases to identify studies examining the relationship between the intersection of at least two marginalized social dimensions and surgical care in LMICs. RESULTS: From 7325 identified abstracts, 31 were included. While none explicitly mentioned intersectionality, an average of 4.3 social determinants were analyzed per study. The most frequently examined were income (100.0 %), location (87.1 %), gender/sex (77.4 %), and education (77.4 %). Beliefs were included in 54.8 % of studies. Other determinants, such as insurance, occupation, race/ethnicity, language, and caste, were less frequently reported. CONCLUSION: Our findings support applying an intersectional lens to understand how social determinants interact, identify the most vulnerable groups, and inform targeted policies that address gaps in access to surgical care.
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 | 0.029 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.029 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".