The relationship and affecting factors between oral health and frailty in the older people: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: To investigate the connection between oral health and frailty in older people and to determine the affecting factors. DESIGN: The research was a community-based cross-sectional study. SAMPLE: A Family Health Center conducted a study on 321 older people. MEASUREMENTS: Data were collected face-to-face using the Personal Information Form, Geriatric Oral Health Assessment Index, and Edmonton Frailty Scale. Factors affecting oral health and frailty were examined using the MANOVA test and the relationship between them was examined using Pearson's correlation test. RESULTS: It was determined that 52.6% of the participants had poor oral health and 56.1% had different levels of frailty. Education was effective on the Geriatric Oral Health Assessment Index scale score. The presence of chronic disease, frequency of tooth/denture brushing, age, education, and sex were effective on the Edmonton Frailty Scale. When the partial eta square values were examined, it was determined that the variable that had the highest impact on the GOHAI and EFS scale scores was educational status. It was determined that there was a significant negative relationship between participants' oral health and frailty scores (r = -0.539, p < .001). CONCLUSIONS: It was determined that more than half of the older individuals included in the study had poor subjective oral health and varying levels of frailty. The influencing factors were determined through multivariate advanced analysis. This relationship and affecting factors are important in providing appropriate early detection and care to older people.
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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 | 0.002 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".