Building a House of Care: Movements Toward an Integration of Neuroscience and Community Solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it