Engaging Knowledge Users with Mental Health Experience in a Mixed-Methods Systematic Review of Post-secondary Students with Psychosis: Reflections and Lessons Learned from a Master’s Thesis
Bibliographic record
Abstract
Engaging knowledge users (KUs) as research team members throughout the research process helps generate relevant knowledge and may improve uptake of research results. The purpose of this article is to describe how an integrated knowledge translation (iKT) approach was embedded within a master's thesis project comprising a mixed-methods systematic review. KUs were engaged in four distinct phases of the systematic review process, including (1) proposal development; (2) development of the research question and approach; (3) creation of an advisory panel; and (4) an end of study meeting to interpret findings and plan dissemination of findings. The extent of each KU's engagement on the research team fluctuated during the study. Challenges included maintaining the same KUs throughout the project and maintaining the scope of the project to align with a master's thesis. Our suggestions for optimizing graduate student iKT projects include having regular team meetings and periodically checking in with team members to encourage reflection on overall engagement and progress of the project. Overall, KUs helped create a research project designed to address their needs and provided input on how results might translate into implications for clinical practice, education, academic policy, and future research within their respective contexts.
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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 | 0.479 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".