“I Want to Meet Other Kids Like Me”: Support Needs of Children with Asthma and Allergies
Bibliographic record
Abstract
CONTEXT: Asthma is the most common chronic illness of childhood and the leading cause of hospitalization in young children. Asthma negatively impacts physical health, psychosocial wellbeing, and quality of life for affected children but the psychosocial support needs of children with asthma and severe have not been studied from their point of view. OBJECTIVE: The objective of this study was to assess the support and education needs and preferred interventions of allergic children with asthma and/or severe allergies. METHODS: Qualitative constant comparative content analysis was used to identify major themes from semi-structured individual interviews with 20 children with asthma and allergies and 35 parents. FINDINGS: Children expressed frustration with the limitations imposed by asthma and allergies on their regular activities and normal lives. Parents believed that peer support--someone to whom their child could relate as a role model--could improve both their children's and their own coping. CONCLUSIONS: Parents and children preferred a combination of in-person meetings and Internet support to enhance the capacity of children, reduce children's anxiety, increase their self-care skills, and self-confidence. In addition they believed a support intervention was a stepping stone to developing a community of support for children with asthma, allergies, and anaphylaxis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".