Building capacity for integrated knowledge translation: a description of what we can learn from trainees’ experiences during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract The use of collaborative health research approaches, such as integrated knowledge translation (IKT), was challenged during the COVID-19 pandemic due to physical distancing measures and transition to virtual platforms. As IKT trainees (i.e. graduate students, postdoctoral scholars) within the Integrated Knowledge Translation Research Network (IKTRN), we experienced several changes and adaptations to our daily routine, work and research environments due to the rapid transition to virtual platforms. While there was an increased capacity to communicate at local, national and international levels, gaps in equitable access to training and partnership opportunities at universities and organizations have emerged. This essay explores the experiences and reflections of 16 IKTRN trainees during the first 2 years of the COVID-19 pandemic at the micro (individual), meso (organizational) and macro (system) levels. The micro level, or individual experiences, focuses on topics of self-care (taking care of oneself for physical and mental well-being), maintaining research activities and productivity, and leisure (social engagement and taking time for oneself), while conducting IKT research during the pandemic. At the meso level, the role of programmes and organizations explores whether and how institutions were able to adapt and continue research and/or partnerships during the pandemic. At the macro level, we discuss implications for policies to support IKT trainees and research, during and beyond emergency situations. Themes were identified that intersected across all levels, which included (i) equitable access to training and partnerships; (ii) capacity for reflexivity; (iii) embracing changing opportunities; and (iv) strengthening collaborative relationships. These intersecting themes represent ways of encouraging sustainable and equitable improvements towards establishing and maintaining collaborative health research approaches. This essay is a summary of our collective experiences and aims to provide suggestions on how organizations and universities can support future trainees conducting collaborative research. Thus, we hope to inform more equitable and sustainable collaborative health research approaches and training in the post-pandemic era.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.034 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.006 | 0.017 |
| 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".