Knowledge, Power, and Technology: A Narrative Review of Knowledge-Sharing in Project ECHO (Extension for Community Health Care Outcomes)
Bibliographic record
Abstract
INTRODUCTION: This narrative review joins an ongoing scholarly conversation about Project ECHO, a virtual educational innovation, with an aim to understand it as a technology-enabled interface that facilitates knowledge-sharing. Critical attention is paid to how dynamics of power are accounted for in the deployment of Project ECHO, and sociomaterial attention is paid to how communication technology is described. The review is relevant to those who wish to establish, understand, or further develop virtual multiprovider knowledge-sharing interfaces. METHODS: Data collection followed processes set out by a narrative review methodology, a review methodology that provides a comprehensive and balanced critical analysis of the published literature. Analysis was informed by thematic analysis. Forty-five articles and one book chapter published between 2015 and 2024 were selected for inclusion. This review was contained to studies of Project ECHO in Canada. RESULTS: Our data set captures an implied awareness of dynamics of power and a desire to counter such dynamics. This is visible in the desire to mitigate a hierarchical differentiation between participants by using teaching and learning theories, a pedagogical design and processes of interaction that favour participation, and by using technology that allows participants to join from across vast geography. DISCUSSION: Emerging scholarly attention to how knowledge is shared and developed in Project ECHO will help develop an understanding of how technology-enabled educational innovations facilitate knowledge sharing in spaces that are imbued with complex power dynamics. Future explicit inclusion of a critical gaze and sociomaterial lens will deepen understandings of technology-enabled educational innovations.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".