Inclusive Physical Activity to Promote the Participation of People with Disabilities: A Preliminary Study
Bibliographic record
Abstract
Background: Physical activity brings improvements in the quality of life in all individuals, disabled and non-disabled. There is little evidence in the literature of inclusive physical activity in which disabled and non-disabled people participate at the same level. Objectives: The study aimed to demonstrate the effectiveness of an inclusive training program, structured in such a way as to encourage physical activity for all participants with and without disabilities, in improving body composition, explosive strength, and endurance. Methods: A sample of twenty-four subjects (mean age: 24.09±3.92 years), 12 disabled and 12 non-disabled, was selected. Quantitative input and output data were recruited at 16-week intervals using a battery of tests: anthropometric measurements, Vertec Squat Jump test, and Yoyo Endurance Test. During the 16 weeks, all participants followed an appropriately structured training program in four mesocycle without any differences. Input and output data were compared employing the t-test for dependent samples. Results and conclusions: The results showed statistically meaningful improvements at an alpha level set at 0.05 for the three parameters tested. These results confirmed the effectiveness of the proposed inclusive training protocol on the improvement of the tested parameters in all participants. These strategies didn't jeopardise the achievement of the overall objectives set; on the contrary, improvements in BMI, explosive strength, and endurance strength of 4.8%, 4.3%, and 56.2% respectively were observed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".