Associations Between Media and Perceptions of Self-Stigma in Persons with Self-Reported Mental Illness and Substance Use Disorder: A Secondary Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Media are highly influential forces that can shape perceptions, beliefs, and values. This is significant as media portrayals of mental/and or substance use disorder have been historically negative and reinforce harmful stereotypes surrounding such conditions. While media’s impact on public stigma toward mental and/or substance use disorder are well documented, less is known about the media’s influence on self-stigma. This thesis investigated the association between negative impact from media on mental health and self-stigma in individuals with mental and/or substance use disorder, with the effect of mass media and social media on self-stigma explored as two distinct objectives. Methods: A secondary cross-sectional analysis was conducted with national survey data collected by Leger in 2022 in collaboration with the Mental Health Commission of Canada and the Bell Mental Health and Anti-Stigma Research chairholder at Queen’s University. The study sample was n= 1334 and n= 1422 for the mass media and social media objectives, respectively. Multivariable linear regression models were used to examine the association between negative impact of media on mental health (mass media or social media, objective dependent) and self-stigma in individuals with mental and/or substance use disorders while adjusting for confounders. Results: For both objectives, moderate-severe perceived negative impact of media on mental health was significantly associated with higher levels of self-stigma after adjusting for confounders (mass media objective: β = 0.33, 95% CI [0.24, 0.41], social media objective: β = 0.24, 95% CI [0.11, 0.37]). In both models, number of formally received diagnoses was a confounder, with more formal diagnoses correlating with higher levels of self-stigma. In the social media objective, sex was a significant effect modifier, with self-stigma scores among those identifying as female being, on average, 0.17 (95% CI [0.01, 0.34]) points higher than those identifying as male. Conclusion: Greater perceived negative impact of media (either mass media or social media) on mental health is associated with higher levels of self-stigma in individuals with mental and/or substance use disorders. These findings highlight the importance of responsible media portrayals of such disorders, as well as interventions aimed at improving media literacy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".