Analyzing Two Decades of Literature on Experiences of Familial Mental Illness Stigma in Four Advanced Countries (2000–2020)
Bibliographic record
Abstract
BACKGROUND: Mental illness-related stigma does not only emanate from the public but also within families of persons with mental illnesses. Familial mental illness stigma implies family members perpetuating stigma against their loved ones with mental illnesses. AIMS: The aim of this review was to analyze the empirical literature on experiences of familial mental illness stigma in four countries. METHODS: Using seven databases, we reviewed 133 empirical studies with 26 meeting the inclusion criteria. Each of the 26 studies spoke to various forms of familial mental illness stigma that potentially impact the self-esteem and self-worth of the affected person. RESULTS: Findings from this review show the existence of familial mental illness stigma in high-income countries, highlighting the need for evidence-based policies to safeguard affected persons at the family level. Close relatives stigmatizing their loved ones due to mental illnesses have contributed to the concealment of mental illness diagnoses within families, which often results in poor prognoses. CONCLUSIONS: Family members' understanding of mental illnesses is key in confronting the stigma associated with mental disorders in our communities, but this is contingent on continuous comprehensive familial program and education. Constant social support from community services and family members is essential in the recovery of persons with mental illnesses. This underscores the need for a stigma-free environment at all levels of society to ensure all-inclusiveness which calls for a comprehensive strategy that targets policy changes, public education, and media representations of mental health-related problems.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".