Psychometric Evaluation of the Young Children's Participation and Environment Measure (YC-PEM) for use in Singapore
Bibliographic record
Abstract
AIMS: To estimate the psychometric properties of a culturally adapted version of the Young Children's Participation and Environment Measure (YC-PEM) for use among Singaporean families. METHODS: This is a prospective cohort study. Caregivers of 151 Singaporean children with (n = 83) and without (n = 68) developmental disabilities, between 0 and 7 years, completed the YC-PEM (Singapore) questionnaire with 3 participation scales (frequency, involvement, and change desired) and 1 environment scale for three settings: home, childcare/preschool, and community. Setting-specific estimates of internal consistency, test-retest reliability, and construct validity were obtained. RESULTS: Internal consistency estimates varied from .59 to .92 for the participation scales and .73 to .79 for the environment scale. Test-retest reliability estimates from the YC-PEM conducted on two occasions, 2-3 weeks apart, varied from .39 to .89 for the participation scales and from .65 to .80 for the environment scale. Moderate to large differences were found in participation and perceived environmental support between children with and without a disability. CONCLUSIONS: YC-PEM (Singapore) scales have adequate psychometric properties except for low internal consistency for the childcare/preschool participation frequency scale and low test-retest reliability for home participation frequency scale. The YC-PEM (Singapore) may be used for population-level studies involving young children with and without developmental disabilities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".