A discourse analysis of social inequities, gender, and stigma in tuberculosis policies of seven countries from Africa, Asia, Europe and South America
Bibliographic record
Abstract
BACKGROUND: Interventions tackling the social aspects of tuberculosis (TB) are widely suggested, yet we miss insights into how policies incorporate these. The language and framing of policies to address TB can lend important insights into how these social drivers are perceived, problematized, and responded to. OBJECTIVE: To understand how discourses in current TB policies frame social dimensions of TB, especially concepts of social inequity, gender, and stigma. METHODS: We conducted a comparative critical discourse analysis of twenty-one publicly available TB-related policies from Belarus, Brazil, Indonesia, Mozambique, Netherlands, Portugal, and Romania, countries with diverse epidemiological, geographical and sociopolitical contexts. Documents were sourced from public websites from May - September 2024. The Bacchi approach was used to analyze policy framings of social inequities, gender, and stigma. RESULT: While policies from Brazil and Indonesia showed greater attention to social inequities, gender, and stigma, and were more explicitly reflective of an equity-oriented and people-centered approach, overall, a dominant biomedical perspective was observed that individualizes responsibility for cure. This tends to disregard issues of social inequity, obscures gender relationships and the multiple dimensions of stigma. At the same time, allocation of individual as well as structural responsibility for TB risk and outcomes co-existed. CONCLUSIONS: Explicit and implicit discourses about TB within health-related policies can influence the nature of attention given to the social dimensions of TB and can shape corresponding responses to the disease. We recommend a participative policy process that includes a broader set of actors to ensure documents are responsive to social realities.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".