Addressing health inequities through social inclusion: The role of community organizations
Bibliographic record
Abstract
Health inequities between groups result from the unequal distribution of economic and social resources, including power and prestige. Social processes where unequal power relationships exist lead to the social exclusion of individuals or groups. Social inclusion strategies are well suited to contribute to addressing health inequities. Community organizations can enhance marginalized community members’ inclusion in decision-making structures that affect their lives. In this paper, we discuss the role of community organizations in contributing to action on health inequities through social inclusion. We consider the social determinants of health and of inequities. We provide an overview of the impact of social exclusion on health inequities and on community capacity to address them. We explore the theoretical basis of addressing health inequities through social inclusion, both in collective action and in research strategies. We link theory to practice with examples from our experiences and describe the challenges of involving members of vulnerable populations. We conclude by offering suggestions as to how community organizations can foster social inclusion and some directions for future research.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".