Co-designing Guidelines for Using Arts-based methods when Conducting Youth Mental Health Research in Online Environments
Bibliographic record
Abstract
Co-designing research-informed guidelines with youth for adapting research methods to other contexts has received little research attention. We report on guidelines co-designed with youth for adapting arts-based methods (ABM) for youth mental health (MH) research in online environments. Seven youth co-researchers participated in 3 co-design workshops and 2 graphic recording focus groups. Data analysis involved a thematic analysis approach. We identified one overarching theme (sustaining mindful presence when conducting research) and 4 subthemes (creating a safe space, youth having a say, facilitating meaningful engagement, paying receptive attention throughout the research process). Facilitating participants’ authentic expression in online environments requires: 1) Letting youth self-identify; 2) incorporating diversity and inclusion; 3) providing accommodations, recognition, and compensation; 4) language considerations; 5) offering ABM training and resources for creating art; 6) using virtual platforms youth use; 7) being mindful of ethical considerations and technology fatigue; 8) addressing barriers in accessing and using technology; 9) providing choice in type of ABM and research methods used; and 10) providing options for communicating during research activities and for engaging in research outside of allotted time (e.g., email, group chat). These research-informed guidelines can be useful for conducting youth MH research and other youth research in online environments.
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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 | 0.299 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".