Factor Structure of Play Creativity: A New Instrument to Assess Movement Creativity
Bibliographic record
Abstract
Few assessment tools have been designed to assess motor creativity, and the existing tools have limitations. To bridge this gap, the current study aimed at designing a new movement creativity assessment tool that considers the unique features underlying the expression of creativity through movement. A modified Delphi technique was used to collect experts’ perspective and derive tasks suitable for evaluation of the main features underlying movement creativity. From these expert ‘consultations, 11 tasks measuring up to 6 movement creativity variables (i.e., fluency, originality, imagination, elaboration, appropriateness, flow) were included in the initial PLAY Creativity measurement model. The COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN checklist) were followed to ensure methodological quality. Overall, 367 children from grade 4 to 6 participated in this study. Results indicated strong test-retest, inter, and intra observer reliability. Confirmatory factor analysis revealed an inadequate fit of the hypothesized model leading to some modifications. After combining originality and imagination, and excluding elaboration, the final measurement model provided an adequate fit. PLAY creativity, in its final form, has adequate validity and can be considered a reliable instrument to assess movement creativity in children. This study thus provides a useful tool to assess and promote movement creativity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".