Association between Caregiver’s and Child’s Oral Health Status and Oral Health Related Quality of Life
Bibliographic record
Abstract
Objectives: To investigate associations between caregivers and children’s oral health, as well as the perceptions of the impact of oral diseases on oral health.\nMethods: Cross-sectional study with 149 child-caregiver dyads from a convenience sample in Toronto. Objective and subjective data about oral health and quality of life were collected from both children and caregivers groups.\nResults: Decay in permanent and primary teeth in children was moderately with missing teeth in caregivers, as well as with most of the Parental-Caregiver Perceptions Questionnaire (P-CPQ). Mostly weak associations were observed between the Oral Health Impact Profile (OHIP-14) scores and missing teeth in caregivers.\nConclusion: Significant associations emphasize the influence of caregivers on children’s oral health outcomes and raise awareness about dental care coverage to disadvantaged families. Despite the questionable findings with some subjective measures, policymakers may still consider the use of patient-centered information to analysing disease trends in the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".