Applying an intersectional lens to alcohol inequities: A conceptual framework
Bibliographic record
Abstract
BACKGROUND: Prior research has demonstrated substantial inequities in alcohol consumption, alcohol-related harms, and mortality. These inequities arise from a complex interplay of factors, unlikely addressed by single factor analyses or solutions. Conceptual frameworks, such as the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework, aim to reflect this complexity and support multifaceted research and action. This paper adapts the NIMHD Framework to focus on alcohol-related inequities and integrate core intersectionality principles. METHOD: We developed the Intersectional Alcohol Inequities Framework (IAIF) through collaboration among leading scholars in alcohol, intersectionality, and policy modelling. In a workshop centred on the core ideas of intersectional frameworks, we identified key factors influencing alcohol consumption and related harms, using the United States as a case study. Using thematic analysis, we grouped the discussion points, then mapped them against the NIMHD Framework. We searched the literature to expand upon workshop insights, iteratively refining the framework until reaching idea saturation. RESULTS: To align with the core ideas of intersectionality, the IAIF introduced new elements absent in the NIMHD Framework, specifically a 'power' domain, a 'historical' level, and emphasis on relationality. We also incorporated a 'digital environment' domain, to reflect an important element of contemporary social context, as previously identified by other health equity scholars. We provided examples of their relevance to alcohol inequities, highlighted practical applications for stakeholders, and discussed adaptability to other public health issues and contexts. CONCLUSIONS: The Intersectional Alcohol Inequities Framework offers a tool for critical dialogue on how various factors, across multiple levels and domains, intersect to influence alcohol-related outcomes. It can provide support and guidance for researchers, facilitate the identification of research needs and gaps in current policies, support the design of new policies and interventions, and inform comprehensive patient management.
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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 | 0.034 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.014 | 0.076 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".