The impact of oral health on body image and social interactions among elders in long‐term care
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore how social interactions and body image are influenced by perceived oral health among older people who live in long-term care facilities. BACKGROUND: Social interactions among frail elders in long-term care (LTC) facilities are limited, but to what extent body image and oral health influence their social relations is poorly understood. A positive body image and the perception of adequate oral health are linked to increased social contacts, as well as improved health and well-being irrespective of age. However, as frailty increases, it is unclear whether appearance and oral health priorities remain stable. MATERIALS AND METHODS: Open-ended interviews were conducted with a purposefully selected group of cognitively intact, older men and women who exhibited varying degrees of frailty, social engagement and oral health conditions and lived in one of seven long-term care facilities. The interviews were analysed using a constant comparative technique, and a second interview with participants checked the trustworthiness of the analysis. RESULTS: Three major categories were expressed by the participants: (1) My mouth is fine; (2) It depends; and (3) Not that important. Within each category, there were several contributing and influencing factors. CONCLUSIONS: Social interactions among residents in LTC may be negatively impacted by poor oral health, but only if other personal and social issues are less bothersome than conditions with the mouth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".