Changing Media Coverage of Mental Illness and Suicide: Results from a Multi-Year Canadian Action Research Study
Bibliographic record
Abstract
ABSTRACT: Evidence suggests that the media plays an important role in shaping public beliefs and attitudes towards mental illness and people with mental illness. On the one hand, holistic and balanced portrayals that focus on treatments and recovery can help reduce stigma and prejudice by increasing knowledge and understanding. On the other hand, sensational and one-dimensional portrayals can create and perpetuate stigmas and stereotypes, which can contribute to prejudice, fear and social exclusion. Related research indicates that the media can also influence suicidal behaviour. On the one hand, research indicates an increase in suicide mortality following romanticized, sensational and detailed media coverage of a suicide (the Werther effect). On the other hand, emerging research indicates a decrease in suicidal mortality following media coverage focused on suicide prevention, available resources and hopeful narratives (the Papageno effect). This presentation will discuss an ongoing national action-research project that has been continuously funded since 2010, which aims to decrease inaccurate and stigmatizing coverage, while increasing hopeful and recovery-oriented coverage, in relation to mental illness and suicide. This will include discussion of (i) longitudinal results from a media monitoring project, examining coverage of mental illness from 2010 to the present; (ii) various educational initiatives targeted at journalists and journalism schools during the project; and (iii) an innovative citizen journalism programme aiming to produce alternative positive portrayals. This presentation will be highly-relevant to people wanting to learn more about media coverage of mental health and suicide, and especially pertinent to people interested in conducting similar research elsewhere. DISCLOSURE OF INTEREST: None Declared
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".