Is the Child Oral Health Quality of Life Questionnaire Sensitive to Change in the Context of Orthodontic Treatment? A Brief Communication
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the ability of the Child Oral Health Quality of Life Questionnaire (COHQoL) to detect change following provision of orthodontic treatment. METHODS: Children were recruited from an orthodontic clinic just prior to starting orthodontic treatment. They completed a copy of the Child Perception Questionnaire, while their parents completed a copy of the Parents Perception Questionnaire and the Family Impact Scale. Normative outcomes were assessed using the Dental Aesthetic Index (DAI) and the Peer Assessment Rating (PAR) index. Change scores and effect sizes were calculated for all scales. RESULTS: Complete data were collected for 45 children and 26 parents. The mean age was 12.6 years (standard deviation = 1.4). There were significant pre-/posttreatment changes in DAI and PAR scores and significant changes in scores on all three questionnaires (P < 0.05). Effect sizes for the latter were moderate. Global transition judgments also confirmed pre-/posttreatment improvements in oral health and wellbeing. CONCLUSION: The results provide preliminary evidence of the sensitivity to change of the COHQoL questionnaires when used with children receiving orthodontic treatment. However, the study needs to be repeated in different treatment settings and with a larger sample size in order to confirm the utility of the measure.
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 | 0.007 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".