Building knowledge translation competency in a community-based hospital: a practice-informed curriculum for healthcare providers, researchers, and leadership
Bibliographic record
Abstract
BACKGROUND: Enacting knowledge translation (KT) in healthcare settings is a complex process that requires organizational facilitation. In addition to addressing organizational-level barriers, targeting individual-level factors such as KT competencies are a necessary component of this aim. While literature on KT competency training is rapidly growing, there has been little exploration of the potential benefits of training initiatives delivered from an intra-organizational perspective. Addressing this gap, we developed the Knowledge Translation Facilitator Network (KTFN) to meet the KT needs of individuals expected to use and produce knowledge (e.g., healthcare providers, research staff, managers, family advisors) within an academic health sciences center. The aim of this study is to describe the development, implementation, and evaluation of the KTFN curriculum. METHODS: An educational framework was used to guide creation of the KTFN curriculum. Stakeholder interviews, a literature review of KT competency, and environmental scan of capacity building initiatives plus adult learning principles were combined with in-house experience of KT practitioners to inform content and delivery. An evaluation strategy consisting of pre/post-test curriculum and post-session satisfaction surveys, as well as post-curriculum interviews assessed impact on participant knowledge and skills and captured perceived value of KFTN. RESULTS: The curriculum has been delivered three times over 3 years, with 30 individuals trained, representing healthcare providers, graduate level research trainees, managers, and family advisors. Using the New World Kirkpatrick Model as an analysis framework, we found that the KTFN curriculum was highly valued and shifted learners' perceptions of KT. Participants identified enhanced knowledge and skills that could be applied to different facets of their work; increased confidence in their ability to execute KT tasks; and intention to use the content in future projects. Barriers to future use included time to plan and conduct KT activities. CONCLUSION: KTFN was developed to enhance KT competency among organizational members. This initiative shows promise as a highly valued training program that meets both individual and organizational KT needs and speaks to the importance of investing in tailored KT competency initiatives as an essential building block to support moving evidence into 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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".