Negotiating familial mental illness stigma: The role of family members of persons living with mental illnesses
Bibliographic record
Abstract
BACKGROUND: This study explores how family members of individuals with mental illnesses address potential familial mental illness stigma. Previous studies have concentrated on self, social, and associative stigma and its impacts on families and persons with mental illnesses. Far less work has considered family members as perpetrators of mental illness stigma towards their loved ones with mental illnesses. METHODOLOGY/PRINCIPAL FINDINGS: We conducted this study with 15 participants who were family members of persons with mental illnesses using semi-structured qualitative interviews. The in-depth interviews were followed by inductive analysis using Braun and Clarke's technique for thematic analysis. Participants' views on familial mental illness stigma and ways to reduce this were reported in five key themes. The themes included: (1) layered perspectives of social and family stigma; (2) family-related stigma; (3) complex interplay of family relationships and mental illness; (4) confronting stigma personally; and (5) envisioning a better future. The uncertainties connected with mental illnesses and the increased social stigma were conceptualized as contributors to familial mental illness stigma as ways to prevent potential associative stigma. CONCLUSION/SIGNIFICANCE: Participants suggested the need for more social contact-based education and positive media reporting to correct the ongoing fallacies around mental illnesses. This study highlights how higher-order reforms to social systems and services would support both families and those living with mental illnesses to have more positive experiences.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".