Engaging youth as co-researchers in virtual qualitative mental health research: Practical guidelines and recommendations
Bibliographic record
Abstract
Engaging youth in research is essential for enhancing the validity and positive impacts of mental health research aimed at benefitting young populations. Yet, youth are infrequently engaged as partners in health research. The current paper describes different ways youth were engaged in research as part of the Digital Divide study, a qualitative study on youth decision-making in accessing digital mental health technologies. The team’s approach was informed by the McCain youth-adult partnership model incorporating principles of flexibility, mentorship, authentic decision-making, and reciprocal learning, which were built in throughout the study. The study involved youth in three ways: as youth study participants, as youth subject matter experts (SMEs), and as employees on the study team in the role of youth research assistant (YRA). Youth study participants provided critical perspectives on youth engagement in research, and their own personal experiences of engaging in research. Both the youth SMEs and YRAs sat on the study steering committee, making critical contributions to study design, implementation, and interpretation of study findings. The YRA’s were also responsible for conducting data collection and contributed to analysis of the study findings. They helped advance equity, inclusion, and accessibility across the different study phases. The research team included social work graduate research assistant coaches that provided YRAs with research coaching and mentorship throughout the study. The involvement of coaches emerged as a powerful youth engagement tool. Youth study participants reported positive experiences in this study being interviewed by YRAs but a lifetime experience of limited engagement in research.
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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 | 0.453 | 0.503 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.015 | 0.022 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".