Establishing priorities in child health: Giving parents and youth a voice
Bibliographic record
Abstract
Parents and youth across Alberta were engaged to identify specific research questions that are a priority to them. Two lists, containing 27 topics were developed with local parent and youth advisory groups, and sent to participants via online questionnaires. Topics were rated from one (least important) to five (most important) and ranked in order of priority. Initial questionnaires were completed by 263 (46%) parents and 308 (54%) youth. Parents rated five topics (behaviour, learning, and developmental disorders; mental health; food, environment and lifestyle; quality of health care; and vaccines) and youth rated four topics (brain and nerve health; mental health; quality of health care; and vaccines) as a high priority. Research questions stemming from 4 parent (12 [5%]) and 6 youth (21 [7%]) focus group discussions were then ranked in a second questionnaire, completed by 43 (43%) parents and 56 (56%) youth. Parents’ highest ranked research question was ‘What is the effect of screen time on cognition and neurodevelopment for children and adolescents?’, while the highest ranked question from youth was ‘What are the early signs of anxiety and depression and when should an individual seek help?’. These topics highlight areas that are important to parents and youth where funding, research, and knowledge mobilization efforts should be directed.
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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 | 0.056 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".