Bibliographic record
Abstract
OBJECTIVE: To evaluate a media intervention designed to improve one newspaper's portrayal of mental illnesses, specifically, schizophrenia. The project was part of an international antistigma program, Open the Doors, organized by the World Psychiatric Association. METHODS: The media intervention attempted to influence news content directly by providing reporters with more accurate background information and helping them develop more positive story lines. The evaluation compared story content and length over a 24-month period: 8 months prior to the antistigma intervention and 16 months postintervention. RESULTS: Positive stories outnumbered negative stories by a factor of 2 in both pre- and postperiods. Positive mental health stories increased by 33% in the postintervention period and their word count increased by an average of 25%. Stories about schizophrenia also increase by 33%, but their word count declined by 10%. At the same time, negative stories about mental illness increased by 25% and their word count by 100%. The greatest increase was in negative news about schizophrenia. Stigmatizing stories about schizophrenia increased by 46%, and their length increased from 300 to 1000 words per story per month. CONCLUSION: The immediate effects of the media intervention were positive, resulting in more and longer positive news stories about mental illness and more positive news stories about schizophrenia. However, when considered from a broader perspective, locally focused efforts yielded meager results in light of the larger increases in negative news, particularly in negative news concerning people with schizophrenia--the target group for the program.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".