Good News? A Longitudinal Analysis of Newspaper Portrayals of Mental Illness in Canada 2005 to 2015
Bibliographic record
Abstract
OBJECTIVES: The overarching aim of this article is to assess media portrayals of mental illness in Canada. We hypothesise that portrayals have improved over time, related to the various antistigma activities of organisations such as the Mental Health Commission of Canada (MHCC). Specific objectives are to assess 1) overall tone and content of newspaper articles, 2) change over time, and 3) variables associated with positive or negative content. METHODS: We collected newspaper articles from print and online editions of over 20 best-selling Canadian newspapers from 2005 to 2015 ( N = 24,570) that mentioned key search terms such as mental illness or schizophrenia. These were read by research assistants, who assessed tone and content for each article using preassigned codes and categories. Data were subjected to chi-squared and trend analysis. RESULTS: Over the study period, 21% of the articles had a positive tone and 28% had stigmatising content. Trend analysis suggested significantly improved coverage over 11 years ( P < 0.001). For example, articles with a positive tone had almost doubled from 2005 (18.9%) to 2015 (34.8%), and articles with stigmatising content had reduced by a third (22.3% vs 32.7%). Analysis also suggested that articles on the front page, as well as articles in broadsheet newspapers, had significantly more positive coverage. CONCLUSIONS: The study indicates that news media coverage related to mental illness has improved over the past decade. This may be related to the concerted efforts of the MHCC, which has executed a targeted strategy aimed at reducing stigma and improving media coverage since 2007.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".