Design, delivery, and evaluation of a knowledge translation intervention for multi-stakeholders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it