Tuberculosis stigma as a social determinant of health: a systematic mapping review of research in low incidence countries
Bibliographic record
Abstract
Tuberculosis (TB)-related stigma is an important social determinant of health. Research generally highlights how stigma can have a considerable impact on individuals and communities, including delays in seeking health care and adherence to treatment. There is scant research into the assessment of TB-related stigma in low incidence countries. This study aimed to systematically map out the research into stigma. A particular emphasis was placed on the methods employed to measure stigma, the conceptual frameworks used to understand stigma, and whether structural factors were theorized. Twenty-two studies were identified; the majority adopted a qualitative approach and aimed to assess knowledge, attitudes, and beliefs about TB. Few studies included stigma as a substantive topic. Only one study aimed to reduce stigma. A number of studies suggested that TB control measures and representations of migrants in the media reporting of TB were implicated in the production of stigma. The paucity of conceptual models and theories about how the social and structural determinants intersect with stigma was apparent. Future interventions to reduce stigma, and measurements of effectiveness, would benefit from a stronger theoretical underpinning in relation to TB stigma and the intersection between the social and structural determinants of health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".