Enhancing children's participation in dental research: A commentary
Bibliographic record
Abstract
The concept of childhood has evolved over the years, inspired by the United Nations Convention on the Rights of the Child in 1989, shifting from developmental models to a conception of childhood that recognizes children as moral agents. This evolution highlights the importance of respecting children's agency and their right to be heard in matters that are related to them. In conventional health research, however, children's voices are often inadequately accessed. In this commentary, we discuss the imperative to recognize children's agency in dental research and a shift from research on children to research with and by children. Moreover, we underscore the importance of actively seeking and listening to children's voices and recognizing their agency in shaping research and healthcare practices in the field of dentistry. Further, we explore the application of participatory research approaches in dental research and provide examples of studies that have involved children in various capacities. We conclude this commentary by emphasizing the potential benefits of participatory research in both qualitative and quantitative dental studies to promote deeper understanding, clearer communication, and stronger advocacy regarding children's interests. Primarily, we call for greater recognition of children's agency in dental research and advocate for more inclusive and child-centred research methodologies.
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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 | 0.046 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.082 | 0.079 |
| Insufficient payload (model declined to judge) | 0.003 | 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".