Youth-developed recommendations on public health planning for future pandemics or public health emergencies: a national Delphi study
Bibliographic record
Abstract
OBJECTIVES: To generate concrete, youth-derived recommendations for government, policymakers, and service planners to support public health planning for the next pandemic or public health emergency. METHODS: Using a virtual, modified Delphi, Youth Delphi Expert Panel Members rated recommendation items over three rounds, with the option to create their own recommendations items. 'Consensus' was defined a priori if ≥ 70% of the entire group, or subgroups of youth (e.g., age, race/ethnicity, gender and sexual identities), rated items at a 6 or 7 (on a 7-point Likert scale). Items that did not achieve consensus were dropped. Content analysis was used for qualitative responses in Rounds 1 and 2. Youth were engaged as members of an expert advisory committee throughout the design, implementation, and interpretation of findings. RESULTS: A total of n = 40 youth participated in Round 1 with good retention (> 95%) in subsequent rounds. Youth endorsed eleven recommendations to support public health planning for future pandemics or public health emergencies. Youth prioritized easily accessible and understandable information about pandemics; equitably and efficiently distributed vaccines; increased awareness of timely and accessible mental health and substance use services in schools, workplaces, and communities; and greater investment in free or inexpensive MHSU services. CONCLUSIONS: For Canada to move forward in a relevant, efficient, and ethically sound manner, decisions must be guided by the population that these decisions affect. These recommendations can be used to guide Canada's strategies and policies to prepare for future public health emergencies and pandemics, prioritizing the needs of youth, families/caregivers, and communities.
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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 | 0.075 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".