Communicating Illness: Depictions of Mental Illness and Cancer in Canadian News Media
Bibliographic record
Abstract
This Major Research Paper (MRP) investigates how mental illness and physical illness are portrayed in Canadian print media and analyzes if and how this contributes to the social stigmatization of mental illness. The MRP explores the following questions: What metaphoric and figurative language is used by the Toronto Star and The Globe and Mail to depict cancer and mental illness? How is authority depicted in newspaper articles about mental illness and physical illness in the Toronto Star and The Globe and Mail? What types of stories about cancer and mental illness are most commonly published by the Toronto Star and The Globe and Mail? A discourse analysis was used to analyze the ways both illnesses were consciously and unconsciously characterized in 58 articles from two of Canada’s most widely circulated newspapers. The quoted authorities and dominant story types were recorded in an attempt to further reveal how both illnesses are framed by the Canadian news media. The results indicated that the most commonly used metaphor within the cancer discourse was the war metaphor. Mental illness was commonly characterized as a loss of control. Patients were quoted significantly more often in articles about cancer than mental illness, suggesting that those with mental illness are not given a prominent voice in characterizing their own illness. Cancer stories were often related to new research. However, crime was most commonly associated with mental illness. These results frame cancer as illness that can be heroically battled collectively. On the contrary, mental illness is framed as a hopeless, personal affliction. These results may suggest that news media depictions of mental illness contribute to the stigmatization of the illness.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".