Relationship between Sense of Coherence, OHRQoL, and Dental Caries among Nursing Students in South India
Bibliographic record
Abstract
Background: Dental caries is one of the most common dental diseases that affect all population and is associated with the avoidance of care. Research has reported that sense of coherence (SOC) is related to many aspects of health including oral health. SOC determines the quality of health and might have a direct association with the development of subjective assessments of oral health. Objectives: To find the association between SOC, Oral Health-Related Quality of Life (OHRQoL) and caries status among nursing college students in southern state of India. Design: Cross-sectional design using questionnaire and assessment of caries status. Participants: Nursing students from south India. Methods: Convenience sampling method was followed and students who were present on the day of the study and consented to participate were included in the study. The total study sample consisted of 494 nursing students. SOC and OHRQoL were measured by a self-administered questionnaire; caries status was assessed using Decayed, Missing and Filled Tooth (DMFT) index. Results: Association between SOC and Oral Health Impact Profile (OHIP) and caries status and OHIP was found to be statistically significant. Correlation between dental caries and OHIP was found to be statistically significant, with R-value -0.251 shows that OHIP is negatively correlated with caries status. Conclusion: SOC as a psychosocial resource is capable of facilitating the motivation for positive oral health behaviours. These resources along with socio-economic and demographic factors can create an environment that is partially responsible for the individuals' cognitive and physical functions that can express themselves as the individuals' well-being and positive health behaviours.
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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 | 0.001 |
| 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 | 0.000 |
| 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".