Integrated knowledge translation guidelines for trainees in health research: an environmental scan
Bibliographic record
Abstract
BACKGROUND: Collaborative health research, such as integrated knowledge translation (IKT), requires researchers to have specific knowledge and skills in working in partnership with knowledge users. Graduate students are often not provided with the opportunity to learn skills in how to establish collaborative relationships with knowledge users in the health system or communities, despite its importance in research. The objective of this environmental scan is to identify available guidelines for graduate trainees to use an IKT approach in their research. METHODS: We conducted an environmental scan with three separate systematic searches to identify guidelines available to support graduate students in engaging in an IKT approach to research: (i) a customized Google search; (ii) a targeted Canadian university website search; and (iii) emails to administrators of graduate studies programmes asking for available guidelines and documents designed for graduate students. Data were extracted using a standardized data extraction tool and analysed using a directed content analysis approach. Due to the minimal results included based on the a priori eligibility criteria, we returned to the excluded records to further review the current state of the environment on trainee support for IKT research. RESULTS: Our search strategy yielded 22 900 items, and after a two-step screening process with strict inclusion criteria three documents met the eligibility criteria. All three documents highlighted the need for an IKT plan for knowledge user involvement throughout the research process. Furthermore, documents emphasized the need for tangible steps to guide graduate students to engage in effective communication with knowledge users. Due to the lack of documents retrieved, we conducted a post hoc content analysis of relevant IKT documents excluded and identified five themes demonstrating increased education and engagement in an IKT approach at an interpersonal and organizational level. CONCLUSION: We identified three documents providing guidance to trainees using a collaborative approach in their health research. This scan highlighted two key findings including the importance of supporting trainees to engage knowledge users in research and preparing an IKT plan alongside a research plan. Further research is needed to co-design guidelines to support graduate students and trainees in engaging in an IKT approach.
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How this classification was reachedexpand
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 | 0.173 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".