Our Journey of Co-Designing a Suicide Stigma Reduction Program for Postsecondary Students: Highlighting the Value of Lived Expert Collaboration in Program Development
Bibliographic record
Abstract
The stigma towards suicide is still very prevalent in our communities. With the increase in reported suicide thoughts and behaviours in postsecondary students, stigma reduction interventions are an important area of mental health promotion. When creating any type of mental health program, people with lived experience (PWLE) should be included in the process. In this article, we share our approach to co-designing a suicide stigma reduction program for postsecondary students, highlighting the advisory group experience within this methodology. The relational approach to recruitment is described, as well as an introduction to each advisory group member and their motivations and experiences with participating in research. We discuss how the advisory group functions, including an outline of our group norms and values, decision-making processes, challenges we experienced, and lessons learned. We end with five recommendations for researchers interested in co-designing programs with PWLE: 1) Be Informed, but Act with Humility; 2) Make Meaningful Connections Early and Often; 3) Strive for Diversity and Representation; 4) Value ALL Forms of Knowledge; 5) Promote Self-Care and Self-Reflection. This article shares broad, personal stories of suicide stigma, but avoids specific details regarding suicide thoughts and behaviours.
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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 | 0.056 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".