ORAL MALODOUR: AN INDICATOR OF ORAL NEGLECT AND POOR SELF-ESTEEM AMONG SLUM DWELLING CHILDREN IN NIGERIA
Bibliographic record
Abstract
Objective: Oral malodour, an inadequately studied disease in children, is the third most prevalent reason for dental consultations, with negative impacts on psychosocial health. This study assessed self-rated and normatively assessed oral malodour and its impact on quality of life among a cohort of slum dwelling children in Lagos, Nigeria. Method: This descriptive survey was conducted among slum dwelling children in Lagos State, Nigeria. A multi-stage sampling method was utilized to select participants for the study. The Organoleptic test was utilized by two calibrated dentists to assess oral malodour. The Self-Reported Scale for Oral Health (SOHO) and the Rosenberg Self-Esteem Scale (RSE) were used to assess quality of life and self-esteem. Regression analysis was done using sociodemographic, clinical, self-esteem and QOL variables as the predictor variables to identify their strength of association with oral malodour. The probability level of p<0.05 was considered significant. Results: Majority of the 427 respondents were aged between 10-14 years of age (67%), and were females (54.4%). Thirty four percent of the respondents self-rated themselves as having oral malodour; Normatively, 22.6% of the respondents had oral malodour. There was a significant association between OHRQOL (aOR: 2.724; CI: 1.563-4.932), gingival inflammation (aOR: 2.402; CI: 1.417-4.078), self-esteem (aOR: 2.546; CI :2.015-5.246), self-rated oral malodour (aOR: 3.846; CI: 2.118-8.571), parental education (aOR: 1.483; CI: 1.034-1.940), history of dental visit (aOR: 8.375; CI: 2.435-28.810), reason for dental visit (aOR: 2.224; CI: 0.932-5.310), and number of children in in the family (aOR: 1.106; CI: 1.010-1.212), with oral malodour. Conclusion: Oral malodour was significantly associated with low self-esteem, poor OHRQOL poor oral health and dental attendance pattern. The oral health of slum dwelling children needs to be seen by policymakers as an important predictor of their mental health and wellbeing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".