“We Need To Be at the Table”: Collaboration with Lived Experts
Bibliographic record
Abstract
BACKGROUND: The recognition of lived experience as an asset has led to increased involvement of individuals most affected by social or medical conditions in research. OBJECTIVES: This paper presents an example of a LE advisory group that co-conceptualized and executed a knowledge mobilization project on aging and homelessness within three Canadian cities (Vancouver, Calgary, and Montreal). METHODS: We established the advisory group, determined the group's priorities and objectives, and fostered community engagement through webinars and in-person events. LESSONS LEARNED: We learned the importance of digital support to enable inclusion of advisors with experiences of homelessness, providing honoraria to for advisors' time and contributions, scheduling meetings on the same day and time each month, and dedicating meeting time for advisors' personal updates and experiences. CONCLUSIONS: This model can be replicated by other research teams studying homelessness, aging, or similar marginalized groups, enhancing the impact of research and knowledge mobilization efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".