Lay Beliefs about Treatments for People with Mental Illness and Their Implications for Antistigma Strategies
Bibliographic record
Abstract
OBJECTIVE: First, to describe factors influencing the public's attitude toward treatment recommendations for people with mental illness; second, to identify coherent belief systems about the helpfulness of specific interventions; and third, to discuss how to ameliorate mental health literacy and antistigma strategies. METHOD: Participants of a representative telephone survey in the general population (n = 1737) were presented with a vignette depicting a person with either schizophrenia or depression. From a list of suggestions, they were asked to recommend treatments for this person. We used a factor analysis to group these proposals and used the factors as the dependent variables in a multiple regression analysis. RESULTS: Treatment suggestions are summarized in 4 groups, each characterizing a specific therapeutic approach: 1) psychopharmacological proposals (that is, psychotropic drugs), 2) therapeutic counselling (from a psychologist or psychiatrist or psychotherapy), 3) alternative suggestions (such as homeopathy), and 4) social advice (for example, from a social worker). Medical treatments were proposed by people who had a higher education, who had a positive attitude toward psychopharmacology, who correctly recognized the person depicted in the vignette as being ill, who were presented with the schizophrenia vignette, who kept social distance, and who had contact with mentally ill people. The variables could explain alternative and social treatment proposals only to a small extent. CONCLUSIONS: The public's beliefs about treatment for people with mental illness are organized into 4 coherent systems, 2 of which involve evidence-based treatments. Medical treatment proposals are influenced by adequate mental health literacy; however, they are also linked to more social distance toward people with mental illness. Additionally, efforts to better explain nonmedical treatment suggestions are needed. Implications for further antistigma strategies are discussed.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".