Impact of the Poor Oral Health Status of Children on Their Families: An Analytical Cross-Sectional Study
Bibliographic record
Abstract
The impact of poor oral health may not just be limited to the children themselves but can impact their families. The current study aims to perform psychometric analyses of the Arabic version of the Family Impact Scale and investigate the association of its domains with the oral health status of children. This cross-sectional study was carried out in a sample of 500 parent-child dyads from high schools of Jazan city of the Kingdom of Saudi Arabia. The Arabic version of the Family Impact Scale was subjected to reliability and validity tests. The explanatory variables in the current study are: the oral health status, parents combined income, parents’ education, age and sex of the child. The descriptive analysis was reported using proportions, this was followed by the bivariate and multivariable analyses. About 24.2% of children were reported to have fair, poor, and very poor oral health. A lower frequency of family impact corresponded with better oral health (OH) status of children (p < 0.001). The likelihood of parent’s taking time off from work and having financial difficulties was nearly two-times greater if their children had poor oral health. Similarly, interruption in sleep and other normal activities of parents is four times and five times greater, respectively, if the child has poor oral health status. Thus, the poor oral health of school children in the Jazan region of Saudi Arabia is a matter of grave concern as it is observed to be associated with family impacts; particularly affecting the parent’s work, sleep, and other normal family activities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".