Elevating Community Care: Building Evaluation Capacity for Brain Health
Bibliographic record
Abstract
Community organizations play a critical role in supporting the complex and dynamic needs of people living with brain health conditions; yet they often lack the capacity to evaluate their work and demonstrate their impacts in ways that resonate with policymakers and traditional healthcare systems. This creates an imbalance in evidence production, which, in turn, often leads to the undervaluing and underfunding of community-based solutions for brain health. Interested in the types of supports intermediary organizations can provide to help community organizations better demonstrate their impacts, this chapter explores a program called GEEK—Growing Expertise in Evaluation and Knowledge Translation—which was created by the Ontario Brain Institute to address asymmetries in evidence production between community organizations and formal healthcare institutions. Through an exploration of the GEEK program and some of the community projects it has supported, we aim to shed light on how investing in community through direct funding and evaluation capacity building can help community organizations demonstrate their value and become better integrated into the broader healthcare system.
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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 | 0.109 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".