Building a House of Care: Movements Toward an Integration of Neuroscience and Community Solutions
Bibliographic record
Abstract
This chapter proposes the House of Care model as an integrative framework for advancing person-centered brain health care through systemic evaluation and community-clinical partnerships. Building on realist evaluation principles, it argues that effective care requires understanding individuals’ lived experiences while addressing structural inequities like those described in the inverse care law, where health care services are inversely distributed with population needs. The House of Care framework emphasizes four interdependent pillars: (1) system-level problem-solving capacities to address root causes of disparities, (2) empowered patients/caregivers engaged as care co-creators, (3) organizational processes enabling cross-sector collaboration, and (4) integrated clinical-community partnerships providing continuous, anticipatory support. The model is applied to critical challenges, including implementing Canada’s Truth and Reconciliation Commission health recommendations through culturally safe evaluations and developing iterative learning through Problem-Driven Iterative Adaptation (PDIA). By combining neurological insights with community wisdom, the approach advocates for epistemic fluency—bridging Western medical and Indigenous knowledge systems to redefine thriving. The chapter positions evaluation as both a diagnostic tool and intervention catalyst, arguing that sustained improvements require dismantling evidence-generation asymmetries between clinical and community sectors while fostering trust through collaborative design. The role of evaluation in building adaptive brain health systems that transcend project-based thinking to help individuals and communities thrive is described.
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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".