Understanding disparities in access to and quality of surgical care for African, Caribbean and Black communities in high-income countries with universal healthcare: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: African, Caribbean and Black (ACB) communities experience disparities in health outcomes, with higher rates of chronic diseases, such as heart disease and stroke, and lower self-reported health status compared to their White counterparts. Barriers to timely access to healthcare services further exacerbate these inequities. Some studies link racialisation to surgical disparities and subpar surgical outcomes. However, the findings are diverse, and there is no synthesis of the evidence on disparities in surgical care for ACB patients in high-income countries with universal healthcare systems. The objective of the scoping review is to systematically describe, characterise and map the existing literature on disparities in the access to and quality of surgical care among ACB patients in high-income countries with universal healthcare systems, and to identify gaps in the literature on surgical access and quality of surgical care in ACB patients. METHODS AND ANALYSIS: The scoping review will follow the Joanna Briggs Institute methodology and report according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. The search strategy will be customised for each database (MEDLINE, Embase, CINAHL, APA PsycINFO and Cochrane Library) using terms for ACB and surgery. Grey literature and references from included studies will be searched for additional sources, with no limitations on publication date or language. All study designs will be eligible. Two independent reviewers will screen titles, abstracts and full texts in duplicate for eligibility. One reviewer will chart data, with a second reviewer validating the data charted. The findings will be synthesised, quantitatively summarised using descriptive statistics and qualitatively analysed through thematic analysis. ETHICS AND DISSEMINATION: Ethics approval is not required as the study utilises published data. The dissemination of the findings will inform future research and improve understanding of the surgical care experiences of ACB patients. Dissemination will target academics and healthcare professionals through publications, presentations and workshops.
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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 | 0.106 | 0.101 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".