Identifying priorities in knowledge translation from the perspective of trainees: results from an online survey
Bibliographic record
Abstract
BACKGROUND: The need to identify priorities to help shape future directions for research and practice increases as the knowledge translation (KT) field advances. Since many KT trainees are developing their research programs, understanding their concerns and KT research and practice priorities is important to supporting the development and advancement of KT as a field. Our purpose was to identify research and practice priorities in the KT field from the perspectives of KT researcher/practitioner trainees. FINDINGS: Survey response rate was 62 % (44/71). Participants were mostly Canadian graduate students, post-doctoral fellows, residents, and learners from various disciplines; the majority was from Ontario (44 %) and Quebec (20 %). Seven percent (5/71) were from other countries including USA, UK, and Switzerland. Seven main KT priority themes were identified: determining the effectiveness of KT strategies, technology use, increased key stakeholder involvement, context, theory, expand ways of inquiry, and sustainability. CONCLUSIONS: Overall, the priorities identified by the trainees correspond with KT literature and with KT experts' views. The trainees appeared to push the boundaries of current KT literature with respect to creative use of communication technologies research.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".