Parental perceptions of children's oral health: The Early Childhood Oral Health Impact Scale (ECOHIS)
Bibliographic record
Abstract
BACKGROUND: Dental disease and treatment experience can negatively affect the oral health related quality of life (OHRQL) of preschool aged children and their caregivers. Currently no valid and reliable instrument is available to measure these negative influences in very young children. The objective of this research was to develop the Early Childhood Oral Health Impact Scale (ECOHIS) to measure the OHRQL of preschool children and their families. METHODS: Twenty-two health professionals evaluated a pool of 45 items that assess the impact of oral health problems on 6-14-year-old children and their families. The health professionals identified 36 items as relevant to preschool children. Thirty parents rated the importance of these 36 items to preschool children; 13 (9 child and 4 family) items were considered important. The 13-item ECOHIS was administered to 295 parents of 5-year-old children to assess construct validity and internal consistency reliability (using Cronbach's alpha). Test-retest reliability was evaluated among another sample of parents (N = 46) using the intraclass correlation coefficient (ICC). RESULTS: ECOHIS scores on the child and parent sections indicating worse quality of life were significantly associated with fair or poor parental ratings of their child's general and oral health, and the presence of dental disease in the child. Cronbach's alphas for the child and family sections were 0.91 and 0.95 respectively, and the ICC for test-retest reliability was 0.84. CONCLUSION: The ECOHIS performed well in assessing OHRQL among children and their families. Studies in other populations are needed to further establish the instrument's technical properties.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".