Dysinclusion: naming and defining the inequitable absence of marginalized populations in health research
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Marginalized populations are frequently absent or invisible in health research. Yet this problem is seldom characterized as a distinct methodological concern. Existing concepts like selection bias or generalizability approach these inequities primarily as technical limitations, not as methodological deficiencies. We introduce dysinclusion to name and define the inequitable absence or invisibility of groups who should be included in research. Our objective is to establish dysinclusion as a distinct concept at the intersection of equity and methods, distinguish it from existing methodological concepts, and examine how it functions, why it matters, and how it can be addressed. METHODS: We draw on examples to define dysinclusion and describe its mechanisms. We differentiate dysinclusion from adjacent epidemiological concepts, and propose three types of dysinclusion processes: data coverage, nonparticipation, and invisibility. RESULTS: Dysinclusion reveals how structural marginalization becomes embedded in research methods. It occurs when marginalized groups are absent from data sources, excluded through study design or barriers to participation, or rendered invisible by measurement and reporting practices. These patterned absences compromise the validity, relevance, and ethical foundation of research. We argue that dysinclusion should be identified and managed not only as a source of bias or threat to validity, but as a central criterion of methodological rigor in the design and implementation of health research. CONCLUSION: Naming dysinclusion challenges the normalization of exclusion and inequity in health research. Dysinclusion offers language to link the ethical concept of equity with research methods. Making dysinclusion visible reframes patterned absence as a threat to both equity and scientific rigor-one that demands deliberate recognition, accountability, and change. PLAIN LANGUAGE SUMMARY: Some groups-like people with disabilities, racialized communities, or those living in poverty-are often missing from health research. Even when they face some of the greatest health challenges, these groups are frequently left out of studies, underrepresented in data, or not even recognized as distinct populations. This absence has serious consequences: it limits what we know about their health, weakens the accuracy of research findings, and can make existing health disparities worse. This problem is common, but there is no widely used method or term in health research to describe or address it. Researchers typically think about who is missing from studies in terms of technical issues like bias or generalizability. These concepts do not fully capture the deeper problem of structural inequality, and make it seem as though ethical concerns, like health equity, are separate from the methods that lead to rigorous science. This paper introduces a new term: dysinclusion. Dysinclusion means the unfair or unjust absence of groups that should be part of health research. It's not just about who is missing-it's about why they are missing and what that says about the way research is designed. We outline three common ways dysinclusion happens: 1) When people are missing from the data we rely on. 2) When people are eligible to participate but cannot or would not. 3) When people are included in a study, but their identity is misclassified, ignored, or made invisible. Dysinclusion is a concept at the intersection of ethics and research methods. Naming and defining dysinclusion can help to guide research that treats equity as a core part of research quality. Just as we assess studies for bias or confounding, we should assess them for dysinclusion, and take steps to reduce it through better study design, more inclusive data collection, and clearer reporting. Addressing dysinclusion is not only about equity. It's also about better science.
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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 | 0.244 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.011 | 0.181 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.007 | 0.036 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".