Measure of Early Vision Use: initial validation with parents of children with cerebral palsy
Bibliographic record
Abstract
PURPOSE: To report initial psychometric evidence on the Measure of Early Vision Use. METHOD: online survey. Psychometric evaluation included assessment of scale dimensionality using Classical Test Theory and hypothesis testing for evidence of construct validity. RESULTS: Principal components analysis of the 14-item parent-rated Measure of Early Vision Use revealed one component with an eigenvalue of 9.343, explaining 66.7% of variance; internal consistency was high (Cronbach's α = 0.96). Total scores ranged from 15-56 (Mean 42.8, standard deviation = 10.6). The results support seven pre-defined hypotheses including statistically significant differences in MEVU-total scores between children with and without parent-reported cerebral visual impairment. CONCLUSIONS: Measure of Early Vision Use is the first assessment tool to describe 'how vision is used' in children with cerebral palsy. Results provide preliminary evidence that the measure comprises a unidimensional construct, sufficient construct validity, and feasibility as a parent-completed online assessment. Findings on internal structure provide foundational evidence and require further testing with Confirmatory Factor Analysis or Rasch Analysis.IMPLICATIONS FOR REHABILITATIONThe Measure of Early Vision Use is a new instrument to describe the use of basic visual abilities and is feasible to use as a parent-completed online questionnaire.The Measure of Early Vision Use is a unidimensional scale with sufficient construct validity to supports its use as a measure of 'how vision is used' without confounding visual ability with the reason why it might be impaired (e.g., cerebral vision impairment, motor limitations, or cognition).There is potential for the Measure of Early Vision Use to support early intervention planning for children with (or at high risk of) cerebral palsy.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".