Assessment of environmental factors in disabled children 2–12 years: development and reliability of the Craig Hospital Inventory of Environmental Factors (CHIEF) for Children–Parent Version
Bibliographic record
Abstract
BACKGROUND: Children with disabilities and their families experience environmental barriers in the school and community environments. There is a need to understand and appropriately measure environmental factors that influence activity and participation for disabled children. The purpose of this paper is to describe the adaptation process of the Craig Hospital Inventory of Environmental Factors (CHIEF) to make it suitable as a parent proxy measure for disabled children aged 2-12 years. METHODS: The adaptation process consisted of four steps using data from previous research conducted at CanChild: (i) analysis of item-total correlations from all items on the CHIEF; (ii) frequency of endorsement; (iii) determination of the representativeness of the questions; and (iv) correlations on selected items. Once the items were selected, a test-retest reliability study was conducted. RESULTS: The internal consistencies (α) for the time 1 and time 2 administrations were 0.76 and 0.78, respectively. Test-retest reliability of the questionnaire was ICC = 0.73 for the total product score. CONCLUSION: The 10-item CHIEF for Children-Parent Version is an acceptable, easy-to-complete and reliable measure of perceived environmental barriers for disabled children 2-12 years of age.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".