Exploratory study about the image of mental illness in health professionals
Bibliographic record
Abstract
Introduction Stigma towards people diagnosed with a mental disorder is a phenomenon that has been observed in different contexts (labor, health, social, media), affecting therefore to different walks of life of the person who has it. One area where greater negative impact exerted by the stigma and discrimination associated is in health care. Objective Knowing what is the image of mental disorders and people who experience them by health professionals from different specialist who don't work in mental health area. Method Exploratory, observational, cross-sectional study. Participants One hundred and fifty medical professionals from different specialties Torrecardenas CH. Instruments Castilian translation of the Opening Minds Stigma Scale for Health Care Providers, developed by the Mental Health Commission of Canada. Score from 0 to 80. Higher scores indicate a stigmatizing attitude. Results Of established comparisons have only found statistically significant differences between men and women in attitudes towards disclosure of diagnosis and seeking help. Conclusions Despite the study's limitations, the data show a trend of response points to a positive attitude towards people diagnosed mental disorder in the health field, while being reflected as feelings of guilt and fear of a possible employment discrimination continue to differentiate mental illness with respect to other diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.004 | 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".