"It's about having the right tools for the job": A qualitative examination of factors related to the uptake and adoption of inclusive physical education resources
Bibliographic record
Abstract
Inclusive Physical Education (IPE) provides an important physical activity opportunity for students with disabilities. Steps to Inclusion (SI) is a teacher training resource specifically designed to facilitate IPE. However, teaching tools and resources, such as SI, can only be effective when systematic and effective adoption is achieved. The Diffusion of Innovations Theory (DOI; Rogers, 2003) provides a useful framework to contextualize and understand factors related to teachers' uptake and adoption of IPE training resources. Guided by the DOI, this study identified factors that teachers perceived to be important in facilitating resource (i.e., SI) uptake and adoption. Participants included Ontario teachers (n=20) at both eth elementary and secondary level. Prior to partaking in semi-structured interviews, participants were provided with an electronic copy of SI and asked to read the document in full. A preexisting coding scheme, based on the DOI, was utilized to perform a deductive thematic analysis and analyze the data. Some predictable patterns and supports for improved uptake and adoption were identified. Communicating and promoting IPE resources to educational leaders (e.g., principals) would facilitate the uptake and adoption by the classroom teacher by capitalizing on the social systems and communication channels in school settings. Providing content from abilities-centered lens along with curated content would provide a relative advantage over current resources, thus improving adoption. Restructuring dissemination approaches to include a web-based platform or hands-on professional development were also suggested as additional strategies to augment adoption. Additional practical implications and directions for future research will be discussed.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".