Crosstalk: Public Cafés as Places for Knowledge Translation Concerning Health Care Research
Bibliographic record
Abstract
This article explores the use of public cafés as a model for knowledge translation and community engagement. We base our discussion on a public café series organized around the theme of access to health care and held in three neighborhoods in the Lower Mainland of British Columbia, Canada. The cafés were part of the Canadian Institutes of Health Research Café Scientifique program. Our purposes for this series of cafés were threefold: (a) to provide a site of communication to connect research with members of the public, (b) to build a network among participants based on common connections to the local community, and (c) to explore through discussion how gendered and raced perspectives concerning access to health care may influence the lived experiences of Canadians today. We intended to promote an intergroup conversation, based on the assumption that people of First Nations descent, newcomers to Canada (whether through immigration or resettlement), and settlers (such as Euro-Canadians) would all benefit from hearing each other's perspectives on access to health care, as well as presentations by invited academics about their research on access to health care. A form of "crosstalk" emerged in the cafés, mediated by gender and ethnicity, where social differences and geographical distances between various groups were not easily bridged, and yet where opportunity was created for inclusive dialogic spaces. We conclude that knowledge translation is not easily accomplished with the café format, at least not with the type of critical knowledge we were aiming to translate and the depth of engagement we were hoping for. Our experiences highlighted three strategies that facilitate knowledge translation: relationships and shared goals; involvement of policymakers and decision makers; and tending to social relations of power.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 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".