An Online Survey to Assess Parents’ Preferences for Learning About Child Health Research 
Bibliographic record
Abstract
Abstract Background: Child health research is crucial to finding safe and effective treatments for children. However, child health research can be challenging in that it can require significant resources. Additionally, parents may need to make the decision to participate in a study during a stressful situation, such as an emergency department visit. Although innovative study design and methodology are being used to try and address these challenges, a key component of conducting more efficient, relevant and successful child health research is finding better ways to engage and involve parents in the research process from study conception to dissemination of results. Methods: We employed a cross-sectional, survey design to seek feedback from parents on 1) how they would like to learn about potential child health research studies that their child could participate in; 2) whether they would like to learn more about the research studies they are participating in; and 3) how they would like to receive information about studies they are participating in. Results: The survey findings demonstrate that parents are interested in hearing about opportunities to participate in child health research, particularly during visits to their general practitioner/pediatrician or walk-in clinics. Most parents would like to receive updates on the progress, results, and researchers involved in studies their child has participated in. Parents would also like to be provided with support to participate in research studies (i.e., travel or child care). Conclusion: This study is part of a larger initiative that is re-examining recruitment and retention methods to inform research teams in planning child health studies. In order to involve a wider range of parents and children in current and future studies, there must be strong engagement strategies in place, developed with parents, to effectively and respectively share research opportunities, progress and results, and demonstrate that their participation matters.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".