The Health Stigma and Discrimination Framework: a global, crosscutting framework to inform research, intervention development, and policy on health-related stigmas
Bibliographic record
Abstract
Stigma is a well-documented barrier to health seeking behavior, engagement in care and adherence to treatment across a range of health conditions globally. In order to halt the stigmatization process and mitigate the harmful consequences of health-related stigma (i.e. stigma associated with health conditions), it is critical to have an explicit theoretical framework to guide intervention development, measurement, research, and policy. Existing stigma frameworks typically focus on one health condition in isolation and often concentrate on the psychological pathways occurring among individuals. This tendency has encouraged a siloed approach to research on health-related stigmas, focusing on individuals, impeding both comparisons across stigmatized conditions and research on innovations to reduce health-related stigma and improve health outcomes. We propose the Health Stigma and Discrimination Framework, which is a global, crosscutting framework based on theory, research, and practice, and demonstrate its application to a range of health conditions, including leprosy, epilepsy, mental health, cancer, HIV, and obesity/overweight. We also discuss how stigma related to race, gender, sexual orientation, class, and occupation intersects with health-related stigmas, and examine how the framework can be used to enhance research, programming, and policy efforts. Research and interventions inspired by a common framework will enable the field to identify similarities and differences in stigma processes across diseases and will amplify our collective ability to respond effectively and at-scale to a major driver of poor health outcomes globally.
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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 | 0.049 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".