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Paper 1: introduction to the series

2024· article· en· W4403897519 on OpenAlexaffabout
Meera Viswanathan, Nila A Sathe, Vivian Welch, Damian Francis, Patricia Heyn, Rania Ali, Tiffany Duque, Elizabeth Terhune, Jennifer S Lin, Ana Beatriz Pizarro, Dru Riddle

Bibliographic record

VenueJournal of Clinical Epidemiology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsBruyèreUniversity of Ottawa
FundersRobert Wood Johnson Foundation
KeywordsSeries (stratigraphy)Equity (law)Health equityActuarial scienceEconometricsSociologyPublic healthEconomicsMedicinePolitical scienceGeologyNursingLaw

Abstract

fetched live from OpenAlex

OBJECTIVES: Systematic reviews hold immense promise as tools to highlight evidence-based practices that can reduce or aim to eliminate racial health disparities. Currently, consensus on centering racial health equity in systematic reviews and other evidence synthesis products is lacking. Centering racial health equity implies concentrating or focusing attention on health equity in ways that bring attention to the perspectives or needs of groups that are typically marginalized. STUDY DESIGN AND SETTING: This Cochrane US Network team and colleagues, with the guidance of a steering committee, sought to understand the views of varied interest holders through semistructured interviews and conducted evidence syntheses addressing (1) definitions of racial health equity, (2) logic models and frameworks to centering racial health equity, (3) interventions to reduce racial health inequities, and (4) interest holder engagement in evidence syntheses. Our methods and teams include a primarily American and Canadian lens; however, findings and insights derived from this work are applicable to any region in which racial or ethnic discrimination and disparities in care due to structural causes exist. RESULTS: In this series, we explain why centering racial health equity matters and what gaps exist and may need to be prioritized. The interviews and systematic reviews identified numerous gaps to address racial health equity that require changes not merely to evidence synthesis practices but also to the underlying evidence ecosystem. These changes include increasing representation, establishing foundational guidance (on definitions and causal mechanisms and models, building a substantive evidence base on racial health equity, strengthening methods guidance, disseminating and implementing results, and sustaining new practices). CONCLUSION: Centering racial health equity requires consensus on the part of key interest holders. As part of the next steps in building consensus, the manifold gaps identified by this series of papers need to be prioritized. Given the resource constraints, changes in norms around systematic reviews are most likely to occur when evidence-based standards for success are clearly established and the benefits of centering racial health equity are apparent. PLAIN LANGUAGE SUMMARY: Racial categories are not based on biology, but racism has negative biological effects. People from racial or ethnic minority groups have often been left out of research and ignored in systematic reviews. Systematic reviews often help clinicians and policymakers with evidence-based decisions. Centering racial health equity in systematic reviews will help clinicians and policymakers to improve outcomes for people from racial or ethnic minority groups. We conducted interviews and a series of four systematic reviews on definitions, logic models and frameworks, methods, interventions, and interest-holder engagement in syntheses. We found that much work remains to be done in centering racial health equity in systematic reviews. Specifically, systematic reviewers need to change who is represented on their teams, establish foundational guidance (on definitions and causal mechanisms and models, identify what interventions work to address racial health equity, strengthen method guidance, disseminate and implement results, and sustain new practices).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.436
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial · Commentary

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".

Quick stats

Citations9
Published2024
Admission routes2
Has abstractyes

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