Enablers of Mental Illness Stigma: A Scoping Review of Individual Perceptions
Bibliographic record
Abstract
Introduction . Stigma is noted to be one of the greatest barriers to the recovery of persons with mental health problems. Stigma has been acknowledged as both an individual and a social orchestration that has an overpowering impact on the social standing of marginalized persons in a society. This study examined the extant literature to ascertain if any evidence(s) suggested a relationship between perceived public attitudes, religious and cultural beliefs, and structural violence in perpetuating stigma against persons with mental illness. Method . We applied a five‐step scoping review framework by Arksey and O’Malley to examine evidence in the literature that suggests relationships between perceptions, religious and cultural beliefs, and structural violence in perpetuating stigma. The researchers systematically conducted a literature search from six databases, including CINAHL, Ovid MEDLINE(R), ProQuest Dissertations & Theses Global, Sociology Collection, PsycINFO, and Sociological Abstracts, using search terms that included stigma, mental illness, perception, religious and cultural beliefs, and structural violence. Results . An initial search in six databases yielded 1223 articles. Checking in the Google Search engine yielded 30 more articles. After removing 25 duplicates, 1198 articles remained for title and abstract screening. After a full‐text review, 1143 articles were removed. Overall, 30 articles were selected for data extraction. Thematic analysis of the extracted data resulted in three main themes. These include perceptions about mental illness, perceptions about stigma and discrimination, and forms of stigma perception. Conclusion . This study revealed that individual perceptions of public attitudes contributed to their construction of stigma. It is incumbent on everyone to play their part in mitigating all the negative outcomes that stigma brings, especially to persons with mental illness.
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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 | 0.027 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".