Self-perceived Oral Health Status, Psychological Well-being, and Life Satisfaction in an Older Adult Population
Bibliographic record
Abstract
Numerous studies have demonstrated that many older adults have problems chewing, pain, difficulties in eating, and problems in social relationships because of oral disorders. However, it is not clear if these functional and psychosocial outcomes affect broader psychological well-being and life satisfaction. Consequently, this paper begins to address the question, 'Does poor oral health compromise the quality of life?'. Initial cross-sectional analyses used data derived from the seven-year follow-up of the Ontario Study of the Oral Health of Older Adults. As at baseline and three-year follow-up, oral health was measured by self-ratings of oral health and five oral health indices. Psychological well-being and life satisfaction were assessed according to the Morale Index, the Perceived Life Stress Questionnaire, The Life Satisfaction Scale, and the General Health Questionnaire. All oral health variables were significantly associated with scores from the first three of these measures in the expected direction. These associations remained after we controlled for other potential influences on the quality of life. In addition, prospective analysis indicated that self-perceived oral health at three years had a significant independent effect on psychological well-being and life satisfaction at seven years. These results suggest that poor self-perceived oral health and relatively poor quality of life co-exist in the same subgroup of older adults.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".