Burns D: In for the long haul: knowledge translation between academic and nonprofit organizations
Bibliographic record
Abstract
Although scientists are continually refining existing knowledge and producing new evidence to improve health care and health care delivery, far too little scientific output finds its way into the tool kits of practitioners. Likewise, the questions that clinicians would like to be answered all too rarely get taken up by researchers. In this article we focus on knowledge translation challenges accompanying a longitudinal research program with nonprofit organizations providing direct and indirect health and social services to disadvantaged groups in one region of Canada. Three essential factors influencing authentic and reciprocal knowledge transfer and utilization between nonprofit service providers and researchers are discussed: strong institutional partnerships, the use of skilled knowledge brokers, and the meaningful involvement of frontline personnel. Keywords community partnerships; knowledge construction; knowledge transfer; knowledge, utilization; vulnerable populations It is commonly acknowledged that there is a significant lag between the production of evidence-based knowledge and health care practice. Although scientists are continu-ally refining existing knowledge and producing new
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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 | 0.039 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".