The use of art as a creative research method to understand psychosocial care needs for children with rare diseases
Bibliographic record
Abstract
Background: Even though a disease might be labelled as ‘rare’, it is estimated that 450 million people globally are affected by rare diseases, and 70% of these conditions are among children. All children have the right to good quality healthcare and to be heard despite the country or state they live in. While children’s drawings are increasingly used in qualitative research to understand children’s experiences and perceptions of illness, few studies in the rare disease field utilize this method. Objective: This study examined drawings of children with rare diseases to gain insight into their experience living with their condition. Design: A qualitative phenomenological research study was employed to explore and understand children’s and young people’s experiences and perceptions of living with rare diseases through research participants’ artistic expression in drawings and responses to semistructured interview questions. Methods: A purposively selected sample of children ( n = 7) attending tertiary paediatric healthcare was invited to participate in a once-off art session facilitated by an art therapist, followed by semi- structured interviews. A practical iterative framework for art-based data analysis was developed to incorporate art interpretations, semi-structured interviews and reflexive analysis. Results: As drawing is an open visual medium, a framework was developed to analyse the drawings thematically. The themes that emerged from the drawings were fitting in versus feeling different and supportive relationships. These themes highlight the contradictory experience of living with a rare disease and the role of family and friends in influencing the participants’ experiences. Conclusion: Developing an art analysis framework benefitted the thematic analysis of the participants’ drawings. This study concludes that art can help offer opportunities for children to express themselves and for health and social care professionals to understand the impact of rare diseases on their everyday lives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".