Measuring Stigma to Assess the Social Justice Implications of Health-Related Policy Decisions: Application to Novel Treatment Regimens for Multidrug-Resistant Tuberculosis
Bibliographic record
Abstract
In making policy decisions with constrained resources, an important consideration is the impact of alternative policy options on social justice. Social justice considers interactions between individuals and society and can be conceptualized across domains of agency, association, and respect. Despite its importance, social justice is rarely considered formally in health policy decision making, partially reflecting challenges in its measurement. We define three criteria for considering social justice in health-related policy decisions: 1) linkage of social justice to a measurable construct; 2) ability to reproducibly and feasibly estimate the impacts of a policy decision on the selected construct; and 3) appropriate presentation to decision makers of the expected social justice implications using that construct. We use preliminary data from qualitative interviews from three groups of respondents in South Africa and Uganda to demonstrate that stigma meets the first of these criteria. We then use the example of policy addressing novel treatment regimens for multidrug-resistant tuberculosis and a validated tuberculosis stigma scale to illustrate how policy effects on stigma could be estimated (criterion 2) and presented to decision makers in the form of justice-enhanced cost-effectiveness analysis (criterion 3). Finally, we provide a point-by-point guide for conducting similar assessments to facilitate consideration of social justice in health-related policy decisions. Our case study and guide for how to make social justice impacts more apparent to decision makers also illustrates the importance of local data and local capacity. Performing social justice assessments alongside more traditional evaluations of cost-effectiveness, budget impact, and burden of disease could help represent data-informed considerations of social justice in health care decision making more broadly.
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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 | 0.046 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".