Taking steps to inclusion: A content analysis of a teaching resource aimed to enhance inclusive physical education
Bibliographic record
Abstract
Background: Teachers are components to successful implementation of school-based strategies to enhance physical activity (PA), such as inclusive physical education (IPE). However, it has been suggested that teachers are insufficiently prepared to conduct PE classes that are inclusive of students with disabilities (SWD). Steps to Inclusion (SI), a teacher resource produced by Ophea, aims to support teachers in achieving IPE. However, a comprehensive review of the resource content has not been conducted. This research is rooted within two guiding theories, a) the Theoretical Domains Framework (TDF) for understanding behaviour change content within interventions and b) indicators of quality PA participation; an experiential conceptualization of PA for SWD. Objective: To analyze the SI to identify theory-based behaviour change content and factors related to quality PA participation for SWD. Methods: Utilizing a deductive approach, a content analysis was performed by two independent coders. Results: With regard to the TDF, the majority of the SI content related to information regarding knowledge (24.1%), environmental context and resources (18.2%), and beliefs about capabilities (18.0%). However, content related to intentions was nonexistent. In relation to the six core elements of quality PA participation, almost half (47.4%) of the content pertained to belongingness. Conclusions: Teachers require an enhanced understanding of IPE along with resources for appropriate implementation. The results suggest that the SI is attempting to fill these gaps. However, SI fails to address intentions, which have been strongly linked to behavioural performance. These findings will inform the design of future supplemental resources.Acknowledgments: CDPP
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".