Bridging the gap between theory and practice: Dynamic systems theory as a framework for understanding and promoting recovery of function in children and youth with acquired brain injuries
Bibliographic record
Abstract
A theoretical framework can help physiotherapists understand and promote recovery of function in children and youth with acquired brain injuries (ABI). Physiotherapy interventions for this population have traditionally been based in hierarchical-maturational theories of motor development emphasizing the role of the central nervous system (CNS) in controlling motor behaviour. In contrast, Dynamic Systems Theory (DST) views movement as resulting from the interaction of many subsystems within the individual, features of the functional task to be accomplished, and the environmental context in which the movement takes place. DST is now a predominant theoretical framework in pediatric physiotherapy. The purpose of this article is to describe how DST can be used to understand and promote recovery of function after pediatric ABI. A DST-based approach for children and youth with ABI does not treat the impaired CNS in isolation but rather emphasizes the role of all subsystems, including the family and the environment, in influencing recovery. The emphasis is on exploration, problem solving, and practice of functional tasks. A case scenario provides practical recommendations for the use of DST to inform physiotherapy interventions and clinical decision making in the acute phase of recovery from ABI. Future research is required to evaluate the effectiveness of interventions based in this theoretical framework.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".