How to Partner With Persons Living With Mental Health Conditions
Bibliographic record
Abstract
SUMMARY STATEMENT: Screen-based simulation is an effective educational strategy that can enhance health care students' engagement with content and critical thinking across various topics, including mental health. To create relevant and realistic simulations, best-practice guidelines recommend the involvement of experts in the development process. We collaborated with persons with lived experience and community partners to cocreate a mental health-focused screen-based simulation. Cocreating meant establishing a nonhierarchical partnership, with shared decision-making from start to finish.In this article, we present 8 principles developed to guide our cocreation with persons with lived experience: person-centeredness, trauma-informed approaches and ethical guidance, supportive environment, two-way partnership, mutual respect, choice and flexibility, open communication, and room to grow. These principles provide practical guidance for educators seeking to engage the expertise of persons who have been historically disadvantaged in society. By sharing these principles, we strive to contribute to a more equitable process in simulation development and promote meaningful, respectful, and safer collaborations.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".