From Sin to Science: Fighting the Stigmatization of Mental Illnesses
Bibliographic record
Abstract
Our paper provides an overview of current stigma discourse, the origins and nature of the stigma associated with mental illnesses, stigmatization by health providers, and approaches to stigma reduction. This is a narrative review focusing on seminal works from the social and psychological literature, with selected qualitative and quantitative studies and international policy documents to highlight key points. Stigma discourse has increasingly moved toward a human rights model that views stigma as a form of social oppression resulting from a complex sociopolitical process that exploits and entrenches the power imbalance between people who stigmatize and those who are stigmatized. People who have a mental illness have identified mental health and health providers as key contributors to the stigmatization process and worthy targets of antistigma interventions. Six approaches to stigma reduction are described: education, protest, contact-based education, legislative reform, advocacy, and stigma self-management. Stigma denigrates the value of people who have a mental illness and the social and professional support systems designed to support them. It creates inequities in funding and service delivery that undermine recovery and full social participation. Mental health professionals have often been identified as part of the problem, but they can redress this situation by becoming important partners in antistigma work.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".