Underlying Dimensions of the Physical Educators’ Judgments about Inclusion Instrument: Brazilian-Version
Bibliographic record
Abstract
Background/Objective: The available information pertaining to the attitudes of Brazilian physical education teachers about teaching students with disabilities is limited in the extant literature base . Rationally, however, scholars argue that d etermining, analyzing, and theorizing about the attitudes of physical education teachers is important in the preparation and professional development of these teachers. It is therefore important to extend the research base using valid and reliable measures regarding teachers’ attitudes about inclusion and teaching students with various disabilities. Purpose: The purpose of this study was to examine the underlying dimensions of the Physical Educators’ Judgments about Inclusion ( PEJI ) scale following translation procedures that converted the existing measure to Portuguese. The PEJI was designed to analyze teachers’ attitudes about inclusion and teaching students with various disabilities. Method: Using a descriptive survey research design, data were collected from 147 participants (women = 57.1%, men = 42.9%) on the PEJI survey instrument. Results : Principal component analysis resulted in salient loadings on three dimensions consistent with prior study using the PEJI and explained 57% of the variance for measuring judgments about: Inclusion versus Exclusion , Acceptance of Students with Disabilities , and Perceived Training Needs . Conclusions : The overall results confirm validity of the PEJI survey consistent with prior study on this measure. The strong magnitude of values indicates that each of the subscales measures separate dimensions of judgments (attitudes). Implications for future use, we recommend reporting individual scale scores rather than a total PEJI score.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".