Individual factors that influence experiences and perceptions of stigma and discrimination towards people with mental illness in Ghana
Bibliographic record
Abstract
People with a mental illness often encounter stigma and discrimination from a variety of sources, reinforcing negative self-perceptions and influencing their health and well-being. Even though support systems and attitudes of the general public act as powerful sources of stigma, views and perceptions held by people with mental illness also influence their sensitivity to the experiences they encounter. The aim of the present qualitative study was to examine perceptions of stigma and discrimination and self-stigma in individuals diagnosed with a mental illness. This study adopted a narrative, descriptive method, using a semistructured interview guide to elicit participant perceptions regarding sources of stigma, discrimination, and personal factors that might influence their experiences. Twelve outpatients attending a clinic in Ghana were interviewed. Thematic content analysis was completed and augmented by field notes. Participants' perceptions about personal impacts of stigma were found to be influenced by self-stigma, anticipated stigma and discrimination, perceived discrimination, and their knowledge about their illness. For many participants, their views served to augment societal views, and thus reinforce negative self-perceptions and their future. However, for other participants, their views served as a buffer in the face of environmental situations that reflect stigma and discrimination. Stigma is a complex, socially-sanctioned phenomenon that can seriously affect the health of people with mental illness. As such, it requires coordinated strategies among public policy makers, governmental bodies, and health-care providers to address stigma on a societal level, and to address its potential impacts on broad health outcomes for individuals 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".