Examining the effects of new members with a physical disability who join an adapted fitness centre: Preliminary results
Bibliographic record
Abstract
People with physical disabilities typically have low levels of physical activity and participation in activities of daily living (ADL). Research shows that physical activity participation can help them increase their level of function, participation in ADL and health-related quality of life (HRQL). The purpose of this study was to preliminary investigate whether members who join an adapted physical activity program improved physical activity levels and participation in ADLs by comparing rates prior to after they begin the program. Using a multiple baseline design, participants (N = 8) completed questionnaires approximately four weeks before starting their program (T1), the day before they began (T2), and two months after starting (T3). We hypothesize no changes between T1 and T2, but improvements between T2 and T3. Cohen's d and descriptive statistics were used for physical activity and ADL data, respectively. A large decrease in physical activity was found between T1 and T2 (d = -0.96), followed by a small increase between T2 and T3 (d = 0.22). Regarding ADLs, participants reported that engaging in the program improved their ADL related to maintaining physical health (Mean = 4.13/5) and general mobility (Mean = 3.87/5). Joining an adapted physical activity program appears to have its expected benefits on new members. However, our sample size is still too small to draw any firm conclusions.Acknowledgments: Our funding comes from a REPAR-OPHQ partnership grant. We would also like to thank Viomax for their help in the realization of this study.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".