The association between mental health self-stigma and multiple mental health conditions: A systematic review
Bibliographic record
Abstract
Background Self-stigma is commonly experienced among people with mental health conditions, across disorders and across the lifespan, with negative impacts. Objective This systematic review aimed to synthesize the literature on self-stigma among people with multiple co-occurring mental health conditions, inclusive of substance use conditions. We further explored whether multiple mental health conditions are associated with an added burden of self-stigma compared to single conditions. Method A systematic search was conducted of Medline, APA PsycInfo, Embase, Cumulative Index to Nursing & Allied Health Literature (CINAHL), Web of Science, and Applied Social Sciences Index and Abstracts (ASSIA). A total of 9246 records were found. We included reports providing quantitative self-stigma scores of individuals with co-occurring mental health or co-occurring mental health and substance use conditions. Quality assessment was conducted. Data were summarized narratively and presented in table format. Results Eleven studies were included with results reported across a sample of 1774 cases. Findings support that substance use conditions may confer an additional burden of self-stigma, but not cannabis use disorder. Self-stigma seems to be high for select comorbidities, in the case of depression, anxiety, and personality disorders, although results are mixed and inconclusive. Negative presentations are associated with higher self-stigma, such as higher symptom levels and illness severity, and lower functioning. Conclusion Substantial self-stigma is associated with comorbid mental health and substance use conditions. However, the literature is not yet fully developed to understand whether and to what degree there might be an additive effect of multiple mental health conditions on self-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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".