The Effectiveness of an e-Learning Program for Educating Physical Activity Professionals on Supporting Autistic Individuals in Physical Activity
Bibliographic record
Abstract
Physical activity (PA) benefits autistic1 individuals, yet barriers including limited support from PA professionals hinder PA engagement. Guided by the theory of planned behavior and the diffusion of innovations theory, this study used a quasi-experimental one-group pretest-posttest design to investigate the effectiveness and practicality of an e-learning program in changing PA professionals' intention and confidence to support autistic individuals in PA. PA professionals (N = 49) completed the Strong Minds Through Active Bodies e-learning program, as well as pre- and postevaluations. Results showed significant improvements in all theory of planned behavior constructs and positive feedback on the module's practicality. Tailored e-learning can enhance PA professionals' understanding and confidence in meeting the unique needs and preferences of autistic individuals in PA. Further research is warranted to understand how e-learning may be leveraged as a tool to foster more inclusive programs, ultimately encouraging greater participation by creating accessible and engaging PA environments that are supportive and conducive to active engagement for autistic individuals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".