The Influence of Dental Insurance on Institutionalized Older Adults in Ranking Their Oral Health Status
Bibliographic record
Abstract
To assess whether dental insurance influences how institutionalized older adults ages 65 and older rank their oral health status, a census survey was designed for residents of Durham's (Canada) Municipal Homes for the Aged. The odds ratio (OR) and the Cochran & Mantel-Haenszel's OR were used to estimate the crude and adjusted effect of dental insurance on oral health status, respectively. Overall, 64 percent participated in the interview. Oral health status was ranked as "good," "very good" or "excellent" by 57 percent of the participants. This ranking was clearly unrelated to the residents having dental insurance, as only 28 percent had dental coverage. Significant effect modifiers included age, dental status and whether the participant had visited the dentist within the last year. Dental insurance positively influenced how dentate participants ranked their oral health status (OR = 2.26; 95 percent CI = 1.19; 4.28). In edentulous participants, age and visiting the dentist within the last year modified the effect of dental insurance on oral health status. Having dental insurance reduced the odds of reporting "good," "very good" or "excellent" oral health (OR = 0.20; 95 percent CI = 0.08; 0.49) among the participants ages 85 and older who did not visit the dentist within the last year; however, the opposite was true for their younger counterparts who visited the dentist within the last year (OR = 7.20; 95 percent CI = 1.08; 47.96). In this population, therefore, dental insurance was associated with higher oral health status rank among the dentate, but its effect on the edentulous population depended on age and the pattern of visiting the dentist.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".