Exploring the Aquatic Environment for Disabled Children: How We Can Conceptualize and Advance Interventions With the ICF
Bibliographic record
Abstract
The aquatic environment provides a unique venue in which children to have an opportunity to thrive. Given the distinct properties of water, swimming not only fosters physical activity but also provides therapeutic benefits for children with disabilities. Over the past decade there has been a substantial increase in the number of articles published on pediatric aquatic interventions. As indicated by the diversity of participants, the various types of aquatic programs, and the 14 new publications that have not yet been synthesized within the literature, it is evident that there is a need to integrate and disseminate the current status of the pediatric aquatics literature. This article explores the findings from a recent scoping review and promotes the utility of the World Health Organization's 2001 International Classification of Functioning, Disability and Health (ICF) as a framework to advance the aquatics field. Specifically, we explore the use of the ICF to address 3 recurring issues within the pediatric aquatics literature: (1) limited descriptions of the aquatic environment; (2) heterogeneity of studies; and (3) variety of outcome measures. It is hoped that future research will adopt the ICF as a conceptual framework to develop and guide the reporting of aquatic interventions.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".