Validation of a French language version of the Early Childhood Oral Health Impact Scale (ECOHIS)
Bibliographic record
Abstract
BACKGROUND: An English language oral health-related negative impact scale for 0-5 year old infants (the Early Childhood Oral Health Impact Scale [ECOHIS]) has recently been developed and validated. The overall aim of our study was to validate a French version of the ECOHIS. The objectives were to investigate the scale's: i) internal consistency; ii) test-retest reliability; iii) convergent validity; and iv) discriminant validity. METHODS: Data were collected from two separate samples. Firstly, from 398 parents of children aged 12 months, recruited to a community-based intervention study, and secondly from 94 parents of 0-5 year-old children attending a hospital dental clinic. In a sub-sample of 101 of the community-based group, the scale was distributed a second time two weeks after initial evaluation. Internal consistency was evaluated through generation of Cronbach's alpha, test-retest reliability through intra-class-correlation coefficients (ICC), convergent validity through comparing scale total scores with a global evaluation of oral health and discriminant validity through investigation of differences in total scale scores between the community- and clinic-based samples. RESULTS: Cronbach's alpha for both the child and family impact sections was 0.79, and for the whole scale was 0.82. The ICC was 0.95. Mean ECOHIS scores for parents rating their child's oral health as "relatively poor", "good" and "very good" were 10.8, 3.4 and 2.7 respectively. In the community- and clinic-based samples, the mean ECOHIS scores were 3.7 and 4.9 respectively. CONCLUSION: These results suggest this French language version of the ECOHIS is valid.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".