Discrepancies between self‐ratings of and satisfaction with oral health in two older adult populations
Bibliographic record
Abstract
OBJECTIVES: General health perceptions, usually measured by means of single-item indicators, are commonly included in health and oral health surveys. The aim of the study reported here was to assess the relationship between self-rated oral health and satisfaction with oral health in two studies of older adult populations. METHODS: Participants in Study 1 were aged 50 years and over, the majority of whom had multiple chronic medical conditions and disabilities and lived within a multi-level geriatric care setting. They were recruited when attending a clinic in that setting for their annual dental screening. Participants in Study 2 were somewhat healthier community dwelling individuals, also aged 50 years and older, who took part. They were originally recruited by means of a telephone survey based on random-digit dialing. For Study 1, data were collected by means of personal interviews and a review of dental clinic charts, while for Study 2 personal interviews, clinical examination and self-completed questionnaires were used. Measures included self-rated oral health, satisfaction with oral health, oral health-related quality of life (OHRQoL) and tooth loss. RESULTS: Data were obtained from 225 persons in Study 1 and 541 in Study 2. In both studies there was a significant association between self-ratings of oral health and satisfaction with oral health. However, also in both studies there was a discrepancy between the measures: approximately 10% of those with favourable oral health ratings were dissatisfied while approximately half of those with unfavourable ratings were satisfied. Those with apparently discordant responses had significantly higher scores on OHRQoL measures such as the GOHAI and the OHIP-14 than those with concordant responses. In Study 2, a similar discrepancy between self-rated general health and satisfaction with general health was also observed. CONCLUSIONS: There is degree of discordance between self-ratings of and satisfaction with both oral and general health status in the older adult populations studied here. This may be because of the expectations concerning health in later life. More needs to be known about the frames of reference people use in constructing their responses to questions designed to assess health perceptions.
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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 | 0.004 | 0.010 |
| 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 | 0.001 | 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".