Design, delivery, and evaluation of a knowledge translation intervention for multi-stakeholders
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) is a key competency for trainees (graduate students and post-doctoral fellows), the new generation of researchers who must learn how to synthesize, disseminate, exchange, and ethically apply knowledge to improve patient and health system services, products, and outcomes. KT training is a key enabler to support KT competency development. Yet, there is a dearth of research on the design, delivery, and evaluation of KT training for trainees. METHODS: The study applied a QUAN(qual) mixed methods approach with an embedded experimental model design. A heart and lung patient was also recruited to participate as a partner and researcher in the study. A multi-faceted KT intervention for trainees was designed, delivered, and evaluated. Data were collected using surveys and focus groups. Quantitative data were analyzed using descriptive and inferential statistics in R Studio and MS Excel. Qualitative data were analyzed in NVivo using thematic analysis. RESULTS: Participation in each KT intervention varied, with 8-42 participants attending KT webinars, 61 attendees in the Three Minute Thesis (3MT) Competition Heat, and 31 participants in the Patient & Public Forum. In total, 27 trainees and 4 faculty participated in at least one of the KT webinars. Trainee participants reported satisfaction, as well as statistically significant increases in 10/13 KT competencies after receiving one or more components of the KT intervention. Additionally, participating faculty, patients, and the public were satisfied with the intervention components they participated in. Several challenges and facilitators were also identified to improve the KT intervention. CONCLUSIONS: The KT intervention is a promising initiative that can be adopted and adapted across various post-secondary settings to support trainees' competency development in KT. This evaluation demonstrates that trainees will respond to opportunities for KT training and that capacity for KT competencies can be advanced through a multi-faceted intervention that involves trainees, faculty, patients, and health system collaborators in its design and delivery. This evaluation study contributes the design and results of a novel KT intervention for multi-stakeholders. TRIAL REGISTRATION: N/A.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Non-randomized trial | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".