Media coverage of mental illness: a comparison of citizen journalism vs. professional journalism portrayals
Bibliographic record
Abstract
Background: Evidence suggests that mainstream media coverage of mental illness tends to focus on factors such as crime and violence. Thus, mental health advocates have argued that alternative portrayals are necessary to reduce stigma.Aim: The aim of this paper is to compare the tone and content of mainstream TV coverage of mental illness with educational videos produced by citizen journalists with mental illness.Methods: We trained three groups of people with mental illness in citizen journalism and participatory video. These groups then produced a series of educational videos about mental illness (n = 26). Simultaneously, we systematically collected TV clips about mental illness from a major Canadian TV station (n = 26). We then compared the tone and content of citizen journalism videos vs. TV clips using content analysis techniques.Results: The citizen journalist videos tended to be more positive and hopeful. For example, over 60% of the citizen journalism videos focused on recovery, compared to 27% of the TV clips. Conversely, over 40% of the TV clips focused on crime, violence or legal issues, in comparison to only 23% of the citizen journalism videos.Conclusion: Citizen journalism by people with mental illness has the potential to educate the public and reduce stigma.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".