From Kisiizi to Baltimore: cultivating knowledge brokers to support global innovation for community engagement in healthcare
Bibliographic record
Abstract
BACKGROUND: Reverse Innovation has been endorsed as a vehicle for promoting bidirectional learning and information flow between low- and middle-income countries and high-income countries, with the aim of tackling common unmet needs. One such need, which traverses international boundaries, is the development of strategies to initiate and sustain community engagement in health care delivery systems. OBJECTIVE: In this commentary, we discuss the Baltimore "Community-based Organizations Neighborhood Network: Enhancing Capacity Together" Study. This randomized controlled trial evaluated whether or not a community engagement strategy, developed to address patient safety in low- and middle-income countries throughout sub-Saharan Africa, could be successfully applied to create and implement strategies that would link community-based organizations to a local health care system in Baltimore, a city in the United States. Specifically, we explore the trial's activation of community knowledge brokers as the conduit through which community engagement, and innovation production, was achieved. Cultivating community knowledge brokers holds promise as a vehicle for advancing global innovation in the context of health care delivery systems. As such, further efforts to discern the ways in which they may promote the development and dissemination of innovations in health care systems is warranted. TRIAL REGISTRATION: Trial Registration Number: NCT02222909 . Trial Register Name: Reverse Innovation and Patient Engagement to Improve Quality of Care and Patient Outcomes (CONNECT). Date of Trial's Registration: August 22, 2014.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".