Advancing motor rehabilitation for adults with chronic neurological conditions through increased involvement of kinesiologists: a perspective review
Bibliographic record
Abstract
Many people with neurological conditions experience challenges with movement. Although rehabilitation is often provided acutely and sub-acutely following the onset of a condition, motor deficits commonly persist in the long-term and are exacerbated by disuse and inactivity. Notably, motor rehabilitation approaches that incorporate exercise and physical activity can support gains in motor function even in the chronic stages of many neurological conditions. However, delivering motor rehabilitation on a long-term basis to people with chronic neurological conditions is a challenge within health care systems, and the onus is often placed on patients to find and pay for services. While neurological motor rehabilitation is largely the domain of physical and occupational therapists, kinesiologists may be able to complement existing care and support delivery of long-term neurological motor rehabilitation, specifically through provision of supported exercise and physical activity programs. In this perspective style review article, we discuss potential contributions of kinesiologists to advancing the field through exercise programming, focusing on community-based interventions that increase physical activity levels. We conclude with recommendations on how kinesiologists' role might be further optimized towards improving long-term outcomes for people with chronic neurological conditions, considering issues related to professional regulation and models of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".