Recommendations for youth engagement in Canadian mental health research in the context of COVID-19.
Bibliographic record
Abstract
The COVID-19 pandemic has resulted in reduced access to in-person mental health services, and a shift to virtual platforms. Youth may be uniquely impacted by physical distancing requirements during the pandemic, including limited socialization opportunities, closures of educational institutions, a lack of meaningful extracurricular activities and adverse implications on key developmental milestones. Due to the potential impact of COVID-19 on youth well-being, the need to rapidly transform services to be accessible, and the potential risks associated with this rapid transformation, it is imperative that youth continue to be engaged in research and service development. Young people's perspectives, strengths and skills need to be considered to effectively adapt the delivery of mental health services. Continuing to center youth engagement in mental health research throughout the pandemic can ensure research questions, programs, and services align with the needs and preferences of youth. In this commentary, we pose three recommendations for conducting youth-engaged mental health research during the pandemic, including adapting youth engagement strategies when rapid decisions must be made, the use of tools for virtual engagement, and suggestions for evaluating youth engagement practices. These strategies and principles may be applicable to other scenarios where rapid research or system transformation would benefit from youth engagement, such as time-limited child research by trainees (e.g., dissertations) or natural disasters.
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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 | 0.150 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".