Oral‐health‐related quality of life among children with and without dental fear
Bibliographic record
Abstract
OBJECTIVES: This study aims to determine whether the oral-health-related quality of life (ORHQoL) and its dimensions differed among children with and without dental fear when different characteristics of fear were measured. MATERIAL AND METHODS: The participants were 11- to 14-year-old Finnish child volunteers from the Oulu University Hospital cleft lip and/or palate (CLP) treatment register (n = 51) and schoolchildren (n = 82). Dental fear was measured with 'Treatment of dental decay' and 'Attending dentist' dimensions of the modified Children's Fear Survey Schedule-Dental Subscale and a single question. ORHQoL was measured with CPQ(11-14). Background variables were gender and the dental care experienced. RESULTS: The response rates were 87% and 51% in the schoolchildren's and CLP groups, respectively. Those who were afraid of 'Treatment of dental decay' had higher mean CPQ(11-14 )total scores (33.2) and higher scores for social (8.8) and emotional well-being (9.0) than those who were not fearful (24.0, 6.2, and 5.8, respectively, P < 0.05), indicating poorer ORHQoL. Among those with experience of orthodontics, the association between dental fear and social and emotional well-being was weaker than among those with no experience of orthodontics. CONCLUSION: Dental fear may have a negative effect on a child's ORHQoL, especially on social and emotional well-being. Positive minor treatment experiences might weaken this effect.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".