Policies and Interventions to Reduce Familial Mental Illness Stigma: A Scoping Review of Empirical Literature
Bibliographic record
Abstract
Although research to date has shown that there can be no health or sustainable development without good mental health, mental illness continues to significantly impact societies. A major challenge confronting people with mental illnesses and their families is the stigma that they endure. In this study, empirical literature was reviewed to assess policies and interventions that seek to reduce familial mental illness stigma across four countries. We used Arksey and O'Malley methodological framework, and a qualitative content analysis was employed to augment the descriptive data extracted. Seven studies published between 2000 and 2020 were analyzed. We propose herein three themes that align with interventions to reduce familial mental illness stigma: transformative education, sharing and disclosure, and social networking and support. The findings indicate that persuasive and purposeful education directed at the public to correct misconceptions surrounding mental illness, with attention to language, may help in reducing familial mental illness stigma. Disclosure of mental illness is encouraged among persons with mental illnesses and their families as a strategy to enhance mutual understanding. Social sharing also affords persons with mental illnesses opportunities to engage with their peers at different levels within the public sphere. Apart from these recommendations, we have noted a paucity of broad governmental-level policies and interventions to comprehensively address the negative attitudes of families toward their relatives. Future work must address this gap to identify effective interventions to create healthier and supportive environments that address familial mental illness stigma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".