Association of Self-Concept with Oral Health Status among Children Residing in Orphanages in Bengaluru
Bibliographic record
Abstract
BACKGROUND Self-concept is a fundamental domain of a person’s psychological function. Relation with family members and peers may influence self-concept which poses children in orphanages at risk. In order to implement and evaluate oral health interventions, we need to consider the functional and psychosocial dimensions of oral health. The purpose of this study was to assess the association between self-concept and oral health status among 12 - 17 year olds residing in orphanages in Bengaluru. METHODS The study was carried out among children living in orphanages. A cluster sampling method was used to select 269 participants from 9 orphanages in Bengaluru. The self-concept of the study participants was assessed using R. K. Saraswat’s selfconcept questionnaire. Oral health status was recorded using world health organization (WHO) oral health assessment form 2013. RESULTS Majority of the children in orphanages had an above average (45 %) and average selfconcept (39 %). Dental caries was present in 91.1 %, missing teeth in 19.7 %, filled teeth in 20.4 %, dental trauma in 17.9, gingival bleeding in 14.5 %, erosion in 5.9 % and oral mucosal lesions in 10.03 % of study participants. CONCLUSIONS Orphan children have an above average self-concept but are at an increased risk for oral diseases. KEY WORDS Self-Concept, Dental Caries, Gingivitis, Trauma, Orphanage
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".