Knowledge translation in health research: A novel approach to health sciences education
Bibliographic record
Abstract
The salient role of knowledge translation process, by which knowledge is put into practice, is increasingly recognized by various research stakeholders. However, medical schools are slow in providing medical students and health professionals engaged in research with the sufficient opportunities to examine more closely the facilitators and barriers to utilization of research evidence in policymaking and implementation, or the effectiveness of their research communication strategies. Memorial University of Newfoundland now offers a knowledge translation course that equips students of community health and applied health research with the knowledge and skills necessary for conducting research, that responds more closely to the needs of their communities, and for improving the utilization of their research by a variety of research consumers. This case study illustrates how the positive research outcomes resulted from implementing the knowledge translation strategies learned in the course. Knowledge translation can be useful also in attracting more funding and support from research agencies, industry, government agencies and the public. These reasons offer a compelling rationale for the standard inclusion of knowledge translation courses in health sciences education.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".