Concordance between caregiver and child reports of children’s oral health‐related quality of life
Bibliographic record
Abstract
OBJECTIVE: This study sought to assess child-caregiver concordance regarding children's oral health related quality of life (OHRQoL) using the Child Oral Health Impact Profile (COHIP). METHODS: The sample comprised treatment-seeking children aged 8-15 with pediatric (n = 141), orthodontic (n = 135), and craniofacial (n = 100) needs and their caregivers. Children and their caregivers were queried concerning the child's Oral health, Functional Well-being, Social/Emotional Well-being, School environment and Self-image. These combined subscales yielded an overall OHRQoL rating. The dyads were distributed at recruitment locations as follows: Montreal (50 pediatric, 13 orthodontic, 15 craniofacial), UMDNJ (45 pediatric, 15 orthodontic, 0 craniofacial), and NYU (46 pediatric, 107 orthodontic, 85 craniofacial). Concordance was assessed with Spearman and intraclass correlations and Kruskal-Wallis testing of categories of agreement. RESULTS: Low to modest rates of agreement between child and caregiver were found for the sample overall. Rates of concordance between child and caregiver varied between clinical groups-craniofacial patients were more likely to rate OHRQoL higher than they were to agree with their caregivers' ratings. In contrast, pediatric and orthodontic patients were more likely either agree with or rate their OHRQoL lower than their caregivers' ratings. CONCLUSION: These findings of child-caregiver concordance using the COHIP supported previous work suggesting the usefulness of obtaining both child and caregiver reports of the child's QoL.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".