Overcoming “You Can Ask My Mom”: Clinical Arts-Based Perspectives to Include Children Under 12 in Mental Health Research
Bibliographic record
Abstract
As research with children (rather than research on children) gains popularity and researchers adapt methods to include children’s voices, continual reflection on the research methods themselves is needed. In this article, we explore the relevance of playing and drawing in qualitative research interviews to include and represent the voice of children under 12 years of age, particularly in the field of mental health research. We reflect on the conception of children’s voice in research and argue for an understanding of voice that goes beyond verbal language. We suggest a combination of perspectives from arts-based research and clinical interview practice to support our understanding of children’s voice in research. As an illustration, we draw on an example taken from a large research project in Youth Mental Health Collaborative Care during which 23 children under the age of 12 were interviewed using a talk-play-draw model. We discuss the multidimensional aspect of children’s voices and the ethical value of arts and play in research interviews. We highlight the importance of researchers’ ethical reflexivity and creative participation in their quest to understand children’s voices. While doing so, we emphasize the responsibility of researchers to interpret, translate and represent as justly as possible a multi-layered, complex and often disorganized voice into a form that is accessible to the linear world of academic research. Given that it is perhaps inevitable that researchers use their own voice in this process, we argue that in conducting research with children, we need to engage both the children as participants and the researchers as advocates for children’s perspectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".