Evaluating the CSAPPA subscales as potential screening instruments for developmental coordination disorder
Bibliographic record
Abstract
OBJECTIVE: In this study, we assess the potential of three subscales of the Children's Self-Perceptions of Adequacy in and Predilection for Physical Activity (CSAPPA), a measure of generalised self-efficacy, as possible screens for developmental coordination disorder (DCD). DESIGN: We used the Bruininks-Oseretsky Test of Motor Proficiency short form (BOTMP-SF) to identify probable cases of DCD. We administered the BOTMP-SF and the CSAPPA to 590 children in grades 4-8 from four schools in the Niagara region of Ontario, Canada. We used receiver operator characteristic (ROC) analysis to assess and compare the performance of the subscales and the full instrument. RESULTS: The area under the receiving operating characteristic curve (AUC), a measure of the overall performance of the test against a diagnostic standard, was good for the full CSAPPA (AUC = 0.81, 95% CI 0.75 to 0.87). The adequacy (AUC = 0.79, 95% CI 0.73 to 0.85) and predilection (AUC = 0.80, 95% CI 0.74 to 0.87) subscales had performance statistically equivalent to the full scale. Since the adequacy subscale is shorter and has good content validity with respect to DCD, we ran additional analyses on this measure. A cut-point of 24 on this subscale gives a sensitivity of 0.86 (95% CI 0.76 to 0.97) and a specificity of 0.47 (95% CI 0.43 to 0.51). CONCLUSION: The adequacy subscale of the CSAPPA appears to be equivalent to the full measure for the purposes of screening for DCD. Further research should explore the possibility of adding further criteria to improve the CSAPPA's modest specificity in this role.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".