Structural validity and internal consistency of an outcome measure to assess self-reported educator capacity to support children with motor difficulties
Bibliographic record
Abstract
Background Partnering for Change (P4C) is a school-based occupational therapy service intended to build the capacity of educators to support children with motor difficulties. Aims This paper describes the development of the Partnering for Change Educator Questionnaire and evaluates its structural validity and internal consistency. Methods and procedures The P4C Educator Questionnaire was completed by 1,216 educators four times across 2 years. Data from the initial time point were analysed via exploratory factor analysis (n = 436). Subsequently, Cronbach’s alpha and mean interitem correlations were calculated. Finally, the proposed factor structure was confirmed by testing it against data from times two through four using confirmatory factor analysis (n = 688). Outcomes and results A three-factor structure was evident and confirmed in hypothesis testing. The factor structure was interpretable according to the framework for building educator capacity used in this study. Internal consistency was high, with the total scale outperforming each subscale. Conclusions and implications A novel measure of educator self-reported capacity to support students with motor difficulties demonstrated structural validity and internal consistency. We currently recommend use as a complete scale accompanied by additional validation research.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".