“WE NEED TO BE AT THE TABLE” COLLABORATION WITH OLDER ADULTS WITH EXPERIENCES OF HOMELESSNESS
Bibliographic record
Abstract
Abstract Increasingly recognized as an asset to research endeavors, persons with lived experience (LE) offer key insights into the impact of research processes and outcomes with those most affected by social or medical conditions. Additionally, LE advisors and co-researchers are uniquely positioned to inform knowledge mobilization and community engagement efforts that complement research activities and disseminate findings to diverse knowledge users. This presentation will share an example of a LE advisory group that collaborated in conceptualizing and implementing a knowledge mobilization project on aging and homelessness within three Canadian cities (Vancouver, Calgary, and Montreal). Following the establishment of the advisory group, priorities and objectives were determined, resulting in community engagement initiatives to disrupt discrimination toward older adults with experiences of homelessness. We will present lessons learned from this project, including the necessity of 1) digital supports to enable inclusion of advisors, 2) honoraria for advisors’ time and contributions, 3) scheduling regular meeting days and times, and 4) dedicating meeting time for personal updates. Insights from this collaborative project are widely applicable across disciplines. This model offers a blueprint for other research teams aiming to enhance the impact of research and knowledge mobilization efforts and build capacity among LE communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".