Priority setting in paediatric preventive care research
Bibliographic record
Abstract
OBJECTIVES: To identify the unanswered research questions in paediatric preventive care that are most important to parents and clinicians, and to explore how questions from parents and clinicians may differ. DESIGN: Iterative mixed methods research priority setting process. SETTING: Toronto, Ontario, Canada. PARTICIPANTS: Parents of children aged 0-5 years enrolled in a research network in Toronto, and clinicians practising in Toronto, Ontario, Canada. INTERVENTIONS: Informed by the James Lind Alliance's methodology, an online questionnaire collected unanswered research questions in paediatric preventive care from study participants. Similar submissions were combined and ranked. A consensus workshop attended by 28 parents and clinicians considered the most highly ranked submissions and used the nominal group technique to select the 10 most important unanswered research questions. RESULTS: Forty-two clinicians and 115 parents submitted 255 and 791 research questions, respectively, which were combined into 79 indicative questions. Most submissions were about nutrition, illness prevention, parenting and behaviour management. Parents were more likely to ask questions about screen time (49 parents vs 8 clinicians, p<0.05) and environmental toxins (18 parents vs 0 clinicians, p<0.05). The top 10 unanswered questions identified at the workshop related to mental health, parental stress, physical activity, obesity, childhood development, behaviour management and screen time. CONCLUSION: The top 10 most important unanswered research questions in paediatric preventive care from the perspective of parents and clinicians were identified. These research priorities may be important in advancing preventive healthcare for children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.485 | 0.509 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".