Data Initiative for the Analysis of Racial/ Ethnic Health Inequalities in Latin American and Caribbean countries: Protocol for a series of Scoping Reviews
Bibliographic record
Abstract
Abstract Background: Racial and ethnic health inequities are a public health concern from a range of structural societal conditions rooted in Racism. The collection of disaggregated race and ethnicity-based data is crucial to understand and appropriately address health inequities. Current data collection efforts remain incomplete and insufficiently widespread. In the Americas, the proportions of Afro-descendants are overrepresented in cardiovascular, maternal mortality and vector-borne diseases. There is limited evidence data on race, ethnicity, and health inequities regarding Latin American and the Caribbean region. Methods: To evaluate the use and scope of population-based race and ethnicity data in health literature. We present a protocol for a series of distinct but interconnected scoping reviews, in the context of racial health inequities across three major health outcomes including i) cardiovascular diseases, ii) maternal, infant, and neonatal mortality, and iii) vector-borne diseases in Latin American and the Caribbean countries between January 1, 2000, to June 30, 2023. Datasets include PubMed/Medline, Embase, CINAHL (EBSCOhost), Global Health, Scopus, LILACS (Virtual Health Library), Web of Science databases and grey literature. We will include cross-sectional, cohort, case-control, surveillance-based, and ecological study designs that analyzed the relationship between race and ethnicity and the selected health outcomes, written in English, French, Spanish, or Portuguese. This protocol is available on the Open Science Framework (Doi: 10.17605/OSF.IO/PE35D). The scoping reviews follow the Joanna Briggs Institute methodology and the Arksey and O'Malley framework. Will be reported in accordance with the Preferred Reporting Items for Systematic Reviews Extension guidelines. Discussion: The series of scoping reviews will systematize and make available the current evidence regarding race and ethnicity inequities in the American and Caribbean region within the context of major health outcomes for a better recognition of knowledge gaps.Results will have critical implications for the documentation of the effect of Racism on health outcomes and shaping racial health inequities observed among these health outcomes, the designed and development of policy action to mitigate and eliminate racial health inequities in the Americas, promoting health equity by making of the invisible, visible.
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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 | 0.150 | 0.189 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.029 | 0.032 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.149 | 0.027 |
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".