Social Contact: Next Steps in an Effective Strategy to Mitigate the Stigma of Mental Illness
Bibliographic record
Abstract
People living with mental illnesses and their families may conceal their conditions to avoid prejudice and discrimination. Stigma often prevents people from receiving adequate health care and other social support services which could exacerbate social and health consequences such as unemployment, homelessness, substance use, and compulsory hospitalization. In this paper, we discuss social contact as a promising anti-stigma strategy for enhancing social interactions among people with mental illnesses, their families, and those without mental illnesses. In particularly, we consider next steps for an approach that works to reduce the stigma-related burden of mental illness. For social contact to be effective in reducing mental illness stigma, it requires broad social buy-in as well as implementation within care systems. Engagement with this approach can be driven through diverse contact-based education using collaborative efforts of society, academic institutions, policy-makers, health professionals, media, and governments. Ultimately, this work aims to consider the next steps in enacting social contact as an anti-stigma strategy through direct interventions and contact-based education. The success of this approach requires pragmatic public policies to support its implementation.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".