Mental health stigma on campus: the promotion of mental wellbeing to Ryerson students
Bibliographic record
Abstract
This MRP looks at the communication used in mental health campaigns for a post-secondary student audience, focusing on how language use and visual design choices impact the stigma associated with mental health. This MRP focuses specifically on the communications seen on Ryerson University’s campus in the 2016 – 2017 academic school year. A video available on Ryerson’s YouTube channel and a sample of posters available throughout campus were analyzed for language and visual design choices to determine how they fit within stigma management communication strategies and how those choices had the potential to influence perceived stigma in viewers." Goffman’s (1963) theory on stigma and an individual’s identity was used to analyze the content of the video and posters. Goffman’s theory outlines the various stages of stigma that an individual experiences, and the impact of each stage on how that individual chooses to interact with others. Miesenbach’s (2010) model for stigma management communication, along with information from an expert interview with a front-line worker will also be used to analyze content in the video and posters. By understanding the communications around mental health through the lens of Goffman (1963) and Miesenbach (2010), it will be possible to understand how the communications are increasing or reducing the stigma around mental health. The analysis of the rhetoric in the messages gives a hint as to how our culture reflects stigma in the messages created, and how this rhetoric may affect students in a culture. This research analyzes Ryerson’s mental well-being campaign for the purpose of identifying a list of best practices for communicating about mental health. The findings show that one of the campaigns accomplishes this better than the other. Effective mental well-being campaigns are those that incorporate elements that normalize discussion of mental health topics, offer strategies for dealing with mental health concerns and overall, promote a culture that prioritizes mental well-being.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".