Visual Thematic Analysis of Children's Illustrations to Improve Receptiveness to Pictorial Asthma Action Plans
Bibliographic record
Abstract
ABSTRACT Background Written asthma action plans are an important component of asthma self‐management. However, these action plans may be difficult for patients with low health literacy, especially children, to understand. The addition of illustrations to health information can improve comprehension, recall and treatment adherence, which can lead to improved disease management. Aim To discern the perceptions of the somatic symptoms of paediatric patients with chronic asthma and aspects of their asthma management. Method Paediatric patients with chronic asthma aged 5 to 13 years who were either inpatients or outpatients of a Canadian paediatric hospital were asked to draw 2 pictures relating to how they felt when their asthma was well‐controlled and how they felt during an asthma attack. The illustrations were evaluated using established visual theme categories. Following theme development, study team members individually analysed the pictures. Results 104 pictures drawn by 53 paediatric participants were analysed. Well‐controlled asthma was commonly depicted by smiling faces (53%), sunshine (33%) and children playing sports outdoors (25%). During an asthma attack, images of sad expressions (60%), children unable to play or confined (28%), children coughing (22%), and lung pain or tightness (19%) were common. Conclusion Paediatric patients' perceptions of their asthma was highly emotional. To improve comprehension and be effective, illustrations added to health information must be specific to the audience.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".