Physical activity promotion to persons with spinal cord injury by health and exercise professionals: A scoping review
Bibliographic record
Abstract
Health and exercise professionals (HEPs) are ideal promoters and valued messengers of physical activity (PA) information among persons with spinal cord injury (SCI). However, little is known about what strategies used by HEPs increase PA behavior in persons with SCI, or what factors influence HEPs when promoting PA to persons with SCI. The purposes of this scoping review were to (1) ascertain the extent, range and nature of the literature, (2) identify strategies used by HEPs that are associated with an increase in PA behavior for persons with SCI, and (3) identify the facilitators and barriers to PA promotion by the HEPs. A comprehensive search was undertaken. Search terms were expanded surrounding three key terms: PA, promotion, and SCI. Twenty-four articles representing 18 unique studies were identified. HEPs predominantly consisted of physiotherapists, occupational therapists, and leisure trainers/therapists. Most interventions were delivered by HEPs to persons with SCI in in-patient rehabilitation centres and community-based settings. Tailored exercise programs and on-going counseling support were considered essential for increasing PA behavior. HEPs’ common barriers to PA promotion were perceived lack of time, education, and training. A need to improve and sustain SCI-specific PA knowledge and education was identified if PA promotion is to become a structured and integral component of practice. This study provides valuable information for interventions to increase PA behavior among persons with SCI by improving PA promotion by HEPs.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".