INNOVATE Research: Impact of a workshop to develop researcher capacity to engage youth in research
Bibliographic record
Abstract
BACKGROUND: Engaging youth in research provides substantial benefits to research about youth-related needs, concerns and interventions. However, researchers require training and capacity development to work in this manner. METHODS: A capacity-building intervention, INNOVATE Research, was co-designed with youth and adult researchers and delivered to researchers in three major academic research institutions across Canada. Fifty-seven attendees participated in this research project evaluating youth engagement practices, attitudes, perceived barriers, and perceived capacity development needs before attending the intervention and six months later. RESULTS: The intervention attracted researchers across various career levels, roles and disciplines. Participants were highly satisfied with the workshop activities. Follow-up assessments revealed significant increases in self-efficacy six months after the workshop (P = .035). Among possible barriers to youth engagement, four barriers significantly declined at follow-up. The barriers that decreased were largely related to practical knowledge about how to engage youth in research. Significantly more participants had integrated youth engagement into their teaching activities six months after the workshop compared to those who were doing so before the workshop (P = .007). A large proportion (71.9%) of participants expressed the need for a strengthened network of youth-engaged researchers; other future capacity-building approaches were also endorsed. CONCLUSIONS: The INNOVATE Research project provided improvements in youth engagement attitudes and practices among researchers, while lifting barriers. Future capacity-building work should continue to enhance the capacity of researchers to engage youth in research. Researchers notably pointed to the need to establish a network of youth-engaged researchers to provide ongoing, sustainable gains in youth engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".