Sharing the neuroscience of living with housing instability: Collaborating with front‐line workers to co‐create a knowledge translation activity
Bibliographic record
Abstract
Abstract Traumatic brain injuries, mental illnesses and neurodevelopmental disorders are established risk factors for housing instability. Further, these brain‐associated conditions may result in additional barriers to accessing services. We explored perspectives of front‐line housing support workers concerning the neuroscience of housing instability to co‐create a knowledge translation (KT) activity. Interviews were conducted with front‐line workers (participant‐researchers) about the impacts of brain‐associated conditions in individuals experiencing housing instability. We combined interview results with existing neuroscience research to develop a KT activity. We collaboratively planned improvements to the KT activity via a focus group. The participant‐researchers found statistics about the overrepresentation of brain‐associated conditions among individuals experiencing housing instability compelling. They expressed that the greatest impact may arise if we target politician and policymaker audiences. Improved awareness of the neuroscience of housing instability may provide motivation for addressing systemic factors that perpetuate the violence of living unhoused.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".