Stigma and Discrimination in People Suffering with a Mood Disorder: A Cross-Sectional Study
Bibliographic record
Abstract
Background. Much research is done on the stigma of mental illness, but little research has been done to characterize these phenomena from the perspective of people with mood disorders. Objective. To characterize the extent to which individuals with bipolar disorder and depression are stigmatized, determine factors related to higher levels of stigmatization, and assess the reliability of the Inventory of Stigmatizing Experiences in a population of people with a mood disorder. Methods. Two hundred and fourteen individuals with depression and bipolar disorder were recruited from a tertiary care psychiatric hospital and surveyed using the Inventory of Stigmatizing Experiences. Results. Participants reported high levels of stigma experiences and this did not differ by diagnosis (P = 0.578). However, people with bipolar disorder reported greater psychosocial impact of stigma on themselves and their family members compared to people with depression (P = 0.019). The two subscales produced internally consistent results with both populations. Conclusion. Stigma negatively affects those with both depression and bipolar disorder but appears to have a greater psychosocial impact on those with bipolar disorder.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".