Are we out of the woods yet? Youth-developed recommendations on recovery from the COVID-19 pandemic: A national Delphi study
Bibliographic record
Abstract
OBJECTIVES: To generate concrete, youth-derived recommendations to support Canada's post-pandemic recovery from COVID-19 to support youth mental health and substance use (MHSU), economic, and educational recovery. METHODS: Using a virtual, modified Delphi, participants rated recommendation items over three rounds, with the option to create their own recommendation items. A priori consensus was defined as ≥ 70% of the entire group, or subgroups of youth (e.g., age, race/ethnicity, gender and sexual identities), rating items at a 6 or 7 (on a 7-point Likert scale). Items were dropped in subsequent rounds if they did not achieve consensus. Qualitative responses were analyzed using content analysis for Round 1. RESULTS: A total of 40 youths participated in Round 1, with good retention (97.5%) in subsequent rounds. Youths achieved consensus on eight recommendations to support post-pandemic recovery. Youths endorsed post-pandemic strategies that prioritize the implementation of effective, accessible, and low-cost MHSU services in schools, workplaces, and communities; the integration of MHSU education into school lessons; increased awareness about MHSU services in schools and workplaces; and the prioritization of health and well-being in schools and workplaces. CONCLUSION: Findings indicate the need for stronger partnerships between schools, community-based MHSU services, and hospitals, and job opportunities that pay a living wage.
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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 | 0.068 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".