Knowledge ‘Translation’ as social learning: negotiating the uptake of research-based knowledge in practice
Bibliographic record
Abstract
BACKGROUND: Knowledge translation and evidence-based practice have relied on research derived from clinical trials, which are considered to be methodologically rigorous. The result is practice recommendations based on a narrow view of evidence. We discuss how, within a practice environment, in fact individuals adopt and apply new evidence derived from multiple sources through ongoing, iterative learning cycles. DISCUSSION: The discussion is presented in four sections. After elaborating on the multiple forms of evidence used in practice, in section 2 we argue that the practitioner derives contextualized knowledge through reflective practice. Then, in section 3, the focus shifts from the individual to the team with consideration of social learning and theories of practice. In section 4 we discuss the implications of integrative and negotiated knowledge exchange and generation within the practice environment. Namely, how can we promote the use of research within a team-based, contextualized knowledge environment? We suggest support for: 1) collaborative learning environments for active learning and reflection, 2) engaged scholarship approaches so that practice can inform research in a collaborative manner and 3) leveraging authoritative opinion leaders for their clinical expertise during the shared negotiation of knowledge and research. Our approach also points to implications for studying evidence-informed practice: the identification of practice change (as an outcome) ought to be supplemented with understandings of how and when social negotiation processes occur to achieve integrated knowledge. This article discusses practice knowledge as dependent on the practice context and on social learning processes, and suggests how research knowledge uptake might be supported from this vantage point.
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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 | 0.020 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".