Trainees’ Self‐Reported Challenges in Knowledge Translation, Research and Practice
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) refers to the process of moving evidence into healthcare policy and practice. Understanding the experiences and perspectives of individuals who develop careers in KT is important for designing training programs and opportunities to enhance capacity in KT research and practice. To date, however, limited research has explored the challenges that trainees encounter as they develop their careers in KT. AIMS: The purpose of this study is to identify the challenges that KT trainees face in their KT research or practice. METHODS: An online survey was conducted with a sample of trainees associated with the Knowledge Translation Trainee Collaborative or the KT Canada Summer Institutes, with written responses thematically analyzed. FINDINGS: A total of 35 individual responses were analyzed, resulting in the identification of six interrelated themes, listed in descending order of prevalence: limited availability of KT-specific resources (54%), difficulty inherent in investigating KT (34%), KT not recognized as a distinct field (23%), colleagues' limited knowledge and understanding of KT (20%), competing priorities and limited time (20%), and difficulties in relation to collaboration (14%). DISCUSSION: KT trainees experience specific challenges in their work: limited understanding of KT in other stakeholder groups; limited structures or infrastructure to support those who do KT; the inherently interdisciplinary and applied nature of KT; and the resultant complexities of scientific inquiry in this field, such as designing and testing multifaceted, multilevel implementation strategies and accounting for contextual factors. LINKING EVIDENCE TO ACTION: KT training and capacity-building efforts are needed to better position health systems to routinely adopt knowledge into healthcare policy and practice.
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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 | 0.037 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".