Designing a knowledge translation mentorship program to support the implementation of evidence-based innovations
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals require training in knowledge translation (KT) to implement evidence-based healthcare innovations. Mentorship is an effective training strategy that could be used to develop KT capacity but it has largely been used to train clinicians. The purpose of this study was to explore preferences for KT mentorship design. METHODS: Interviews were conducted with 54 Canadian researchers and research users who varied by profession, department, career stage and sex. Participants were asked about KT needs, views on mentorship as a strategy to develop KT capacity, and suggestions for program design. Grounded theory technique and thematic analysis were used to collect and analyse data. RESULTS: Participants uniformly expressed interest in mentorship over other forms of learning about KT because it would provide credible, tailored information when needed. A variety of options for program content, format and delivery were recommended, suggesting the need for flexibility according to KT needs. Leadership, infrastructure, culture change and incentives may also be needed to foster KT mentorship. Views were mixed on whether mentors should be KT experts or subject or clinical experts with KT experience, and embedded in, or external to organizations. CONCLUSIONS: These findings can be used to develop or evaluate KT mentoring programs. Further research is needed to evaluate different models in which the mentor may be an internal or external KT expert or subject expert with experience in KT, and establish the core curriculum of a training program specific to KT and how it could best be reinforced with mentoring.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".