Stigma experienced by people with mental illness in South America: an integrative review
Bibliographic record
Abstract
Introduction: People with mental illness are highly stigmatized by populations around the world and are perceived to be a burden on society. As a result of stigma, many people with mental illness are discriminated against, which leads to limited life opportunities. Given that beliefs about mental illness can vary based on culture, religion, nationality and ethnicity, it is important to understand the different types of mental illness-related stigma experienced around the world. Materials and Methods: Whittemore and Knafl's (2005) methodology for integrative reviews was used to analyze 18 studies about lived experiences of mental illness-related stigma in South America. Results: Findings suggest that certain types of stigma in South America are based on gender and social norms, such as the social position of men and women in society. This leads to discrimination, isolation and violence from family, intimate partners, friends, society and health professionals. Employment is also limited for South Americans with mental illness. Other consequences, such a self-stigma, also impact the lives of people with mental illness in many South American contexts. Discussion: Family, friendship and social relationships, including health professionals, can involve processes that lead to the stigma experienced by people with mental illness. Conclusion: This integrative review highlights how mental illness related-stigma impacts individuals in South America.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".