Correlation of the Pediatric Volitional Questionnaire with the Test of Playfulness in a virtual environment: the power of engagement
Bibliographic record
Abstract
The Pediatric Volitional Questionnaire (PVQ) was used along with the Test of Playfulness (TOP) to assess 16 children with cerebral palsy who took part in a study of virtual reality play intervention. Both observational measures are designed to assess children as they are engaged in occupations in one or more environments. Virtual reality offers an alternative play environment for children who have disabilities. It eliminates several physical barriers usually encountered in real life. It also is a powerful medium for engaging and providing a sense of control and enjoyment with the tasks engaged with. Several virtual environments and activities were offered to the children over an eight‐week period. The purpose of this paper is to examine the relationship between these two measures that were used to assess aspects of motivation and playfulness, and to explore which aspects of these measures are most correlated when assessing children in virtual environments. The Pearson correlation calculated between the average motivation score of the TOP and the average PVQ score was significant (r = .47, p = .05). The item correlations were all non‐significant except for two. These were item 6 ‘stays engaged’ (r = .51, p = .03) and item 9 ‘tries to produce effects’ (r = .55, p = .02). There is some evidence that these two measures are tapping into similar constructs. These results 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".