Social perceptions and the stigmatization towards fifteen mental illnesses in France: a preliminary study on the role of vital force and burden
Bibliographic record
Abstract
Introduction This study examined social perceptions and rejection towards fifteen mental illnesses, as well as a preliminary test of the SUBAR model, that hypothesized perceptions of both vital forces and burden would be negatively and positively related to social rejection, respectively. Methods Using an online survey with participants from France ( n = 952), social rejection was assessed using a feeling thermometer and a social distance scale, while social perceptions were measured using visual analog scales. Results A stigma map for these different disorders is drawn up, revealing the social perceptions and levels of stigmatization specific to certain mental illnesses. Controlling for relevant social perceptions (i.e., danger, warmth, competence), we found that perception of burden was positively and significantly associated to social distance and negative feeling for 73% and 67% of mental illnesses, respectively. The perception of vital force was negatively and significantly related to social distance and negative feeling for 87% and 20% of mental illnesses, respectively. The change in R2 between model 1 (i.e. perception of danger, warmth, competence) and model 2 (i.e. model 1 + perceptions of vital force and burden) significantly improved in 100% of cases for social distance and 67% of cases for negative feeling. Conclusion These preliminary data provide support for the SUBAR model and call for further investigations to better understand the social rejection of people with mental illnesses.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".