When mental health care is stigmatizing: A participative study in schizophrenia.
Bibliographic record
Abstract
OBJECTIVES: Mental health care has been identified as a major source of mental illness stigmatization. Detailed information about these stigmatization experiences is thus needed to reduce stigma in mental health practices. The study aimed to (a) identify the most relevant stigmatizing situations in mental health care encountered by users with schizophrenia and their families; (b) characterize the relative importance of these situations in terms of frequency, experienced stigmatization, and associated suffering; and (c) identify contextual and individual factors associated with these experiences. METHOD: An online survey was conducted in France among users and family members to characterize situations of stigmatization in mental health care and identify associated factors. The survey content was first developed from a participative perspective, through a focus group including users. RESULTS: A total of 235 participants were included in the survey: 59 participants with schizophrenia diagnosis, 96 with other psychiatric diagnoses, and 80 family members. The results revealed 15 relevant situations with different levels of frequency, stigmatization, and suffering. Participants with a diagnosis of schizophrenia experienced more situations of stigmatization, with a higher frequency. Moreover, contextual factors were strongly associated with experienced stigmatization, including recovery-oriented practices (negatively associated) and measures without consent (positively associated). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: These situations, as well as associated contextual factors, could be targeted to reduce stigmatization and related suffering in mental health practices. Results strongly underscore the potential of recovery-oriented practice as an instrument to fight stigma in mental health care. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".