Program evaluation of a virtual physical activity program for individuals with disabilities
Bibliographic record
Abstract
Introduction: Regular physical activity is important for positive health outcomes yet, most individuals do not meet physical activity guidelines. Recent studies show that one in five Canadians aged 15 or older have one or more disabilities, yet as a population, individuals with disabilities are 16%-62% less likely to meet physical activity guidelines. The COVID-19 pandemic created additional barriers to physical activity participation as lockdowns prevented in-person programming. In response to the pandemic, the Acadia University Sensory Motor Instructional Leadership Experience (S.M.I.L.E.) Program shifted its programming to a virtual platform; however, there was little research to guide its creation, implementation, or expected outcomes. Thus, this program evaluation explored program feasibility and impact on physical activity and physical literacy. Method: A mixed methods case study approach was used for this project. Virtual S.M.I.L.E. took place over eight weeks in the fall of 2020. Programming consisted of three live Zoom sessions facilitated by trained program leaders, and eight weeks of at-home activity guides for participants to complete on their own time. Demographic data, physical literacy (PLAYself), and physical activity (IPAQ-A) data were collected using caregiver pre-and post-programming surveys. Throughout programming, weekly check-in surveys were sent to reflect on the previous week of programming. After the eight weeks of programming were complete, caregiver and leader interviews were conducted to understand both program implementation and performance perspectives. Results: = 0.03). Caregiver and leader interviews highlighted five main themes following the virtual programming: (a) Virtual impact on programming; (b) Programming impact on social and motor goals; (c) Impact of program design; (d) Impact on physical activity; and (e) Program feasibility for families. Discussion: Results from this program evaluation suggest that physical literacy and physical activity levels were generally maintained throughout programming and caregivers indicated several social and activity benefits. Future work includes program modifications and further evaluation of virtual adapted physical activity programming to improve the physical literacy of individuals with disabilities.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".