Coping with paediatric illness: Child’s play? Exploring the effectiveness of a play- and sports-based cognitive behavioural programme for children with chronic health conditions
Bibliographic record
Abstract
Little is known about how play affects the development of children with a chronic condition. Studying play poses major methodological challenges in measuring differences in play behaviour, which results in a relative scarcity of research on this subject. This pilot study seeks to provide novel directions for research in this area. The effectiveness of a play- and sports-based cognitive behavioural programme for children (8–12 years) with a chronic condition was studied. The children and parents completed a battery of measurement tools before and after the programme. Moreover, the application of automated computer analyses of behaviour was piloted. Behaviour (Child Behavior Checklist) seemed to be positively affected by the programme. An increase in psychological well-being was observed (KIDSCREEN). Perceived competence (Self-Perception Profile for Children) and actual motor competence (Canadian Agility and Movement Skill Assessment) did not show any positive trends. These results of 13 participants suggest that children might learn to better cope with their illness by stimulating play behaviour. For the analysis of the effectiveness of programmes like this, we therefore propose to focus on measuring behaviour and quality of life. In addition, pilot measurements showed that automated analysis of play can provide important insights into the participation of children.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".