Unveiling the Hidden Barriers: A Review of Stigma Associated with Infectious Diseases and Its Impact on Prevention and Control
Bibliographic record
Abstract
A BSTRACT This review explores the influence of stigma on infectious diseases like tuberculosis and sexually transmitted infections and its implications for public health efforts. A comprehensive search of peer-reviewed articles provided data on stigma concepts, contributing factors, cultural and social contexts, and health outcomes. Using tools like the Newcastle-Ottawa Scale and CASP checklists, we assessed study quality and calculated odds ratios (ORs), confidence intervals (CIs), and P values to measure stigma’s impact. Among 35 included studies, findings reveal stigma in Saudi Arabia is deeply entrenched in cultural and social frameworks, contributing to delayed diagnosis (ORs 1.5–3.0, CIs 1.2–4.0, P < 0.01), reduced healthcare utilization, and poor outcomes. Key factors include misinformation, cultural beliefs, and fear of social rejection. Effective interventions such as public education, community engagement, and policy reforms are crucial. Addressing stigma through culturally sensitive public health strategies and policy advocacy is vital for improving disease prevention and control.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".