What do children's global ratings of oral health and well‐being measure?
Bibliographic record
Abstract
OBJECTIVES: To explore the constructs children incorporate in the responses to global ratings of their oral health (OH) and OH-related overall well-being (OWB). METHODS: Data were collected as part of a project to validate the Child Perceptions Questionnaire for ages 11-14 (CPQ11-14), a self-report measure of OH-related quality of life. Its 37 questions are organized in the symptoms, functional limitations, emotional and social well-being domains. Children were recruited from paediatric dentistry, orthodontic and orofacial dental clinics. To identify the CPQ11-14 domain scores and questions predicting the global ratings, correlation and multiple regression analyses were used. RESULTS: Of the 123 children, 22.8% rated their OH as 'Fair/Poor' and 30.1% reported that their OWB was affected by their oral/orofacial condition. Positive significant correlations were observed between the OH ratings and the CPQ11-14 oral symptoms and emotional well-being domains, and between the OWB ratings and all four CPQ11-14 domains. The number of the CPQ11-14 questions significantly correlated with the OH and OWB ratings were 8 and 19, respectively. Only the symptoms domain entered the model for the OH (R2=0.05), while age, functional limitations and emotional well-being domains predicted the OWB (R2=0.18). The OH model included three questions (R2=0.13) and the OWB model included age and six questions (R2=0.25). In both models all but one of the questions came from the emotional and social well-being domains. CONCLUSIONS: These findings suggest that children view OH and its impact on well-being as multidimensional concepts. Further research, including qualitative studies, is needed to better understand the referents children use when responding to global ratings and the factors that determine their responses.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".