Mental disorders in the media: A retrospective study of newspaper coverage in the Chilean Press
Bibliographic record
Abstract
BACKGROUND: The mass media are relevant in shaping the population's attitudes towards mental disorders. In low- and middle-income countries there is little information about the portrayal of people with mental disorders in the mass media. AIM: The general objective of the study was to assess the tone and content of Chilean newspaper articles about mental disorder from 2000 to 2019. METHOD: The digital editions of four national circulation Chilean newspapers were intentionally selected. The search engine Google News was used to identify and retrieve the news. To evaluate the news, a standardised codebook was administered. A total of 385 news were evaluated. RESULTS: The results show that a large proportion of the news items has an overall positive/optimistic tone 43.5% and 57.5% does not stigmatise; however, only 18.4% emphasises recovery as part of the content. The highest percentages of news stigmatising in tone and content are observed for bipolar disorder and schizophrenia. Furthermore, the experts are quoted much more frequently than people diagnosed with mental disorders or their families and/or friends. When comparing by years there is a trend towards general decrease in stigmatisation, moving towards a more positive or optimistic view of mental health. CONCLUSIONS: In general, low stigmatisation towards mental disorders was found in the news and this was reduced steadily over time. Although there are aspects to improve in some particular areas, suggesting that manifest stigma has diminished, but more subtle forms still remain.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".