Building capacity in quantitative research and data storytelling to enhance knowledge translation: a training curriculum for peer researchers
Bibliographic record
Abstract
BACKGROUND: Many community-based HIV research studies incorporate principles of greater involvement and meaningful engagement of people living with HIV (GIPA/MEPA) by training people with HIV as peer researchers. Unfortunately, there are still some aspects of research (e.g., quantitative data analysis and interpretation) where many projects fall short in realizing GIPA/MEPA principles. To address these gaps, we developed an eight-week training course that aimed to build the capacity of peer researchers around the understanding and interpretation of quantitative data and incorporating lived experience to increase the impact of the knowledge transfer and exchange phase of a study. METHODS: Peer researchers (n = 8) participated from British Columbia, Alberta, and Ontario and lessons learned from the training were implemented throughout the dissemination of research findings from the People Living with HIV Stigma Index study. This paper presents the curriculum and main training components, course evaluation results, and challenges and lessons learned. The manuscript was created in collaboration with and includes the perspectives of both the peer researchers involved in the training, as well the course facilitators. RESULTS: Throughout the course, peer researchers' self-assessed knowledge and understanding of quantitative research and data storytelling improved and, through interactive activities and practice, they gained the confidence to deliver a full research presentation. This improved their understanding of research findings, which was beneficial for discussing results with community partners and study participants. The peer researchers also agreed that learning about integrating lived experience with quantitative data has helped them to make research findings more relatable and convey key messages in a more meaningful way. CONCLUSIONS: Our training curriculum provides a template for research teams to build capacity in areas of research where peer researchers and community members are less often engaged. In doing so, we continue to uphold the principles of GIPA/MEPA and enhance the translation of research knowledge in communities most greatly affected.
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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 | 0.105 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".