Oral health status and possible explanatory factors of an inner-city low-income community
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Individuals with low income bear a number of health challenges to healthcare services. Vancouver's Downtown Eastside (DTES) is known to be a low-income community in a metropolitan city. Because it is difficult to reach, the oral health (OH) status of these residents is unknown. The objectives of this study are (1) to design a tool and strategy to collect OH information in a low-income community, (2) to characterize the OH status and related factors among low-income adults, and (3) to identify the explanatory factors for their OH status. MATERIALS AND METHODS: Mobile screening clinics were established in the gathering centers of the DTES, and those of 19 years of age or older were recruited. Data were collected through survey interviews and clinical examinations. Potential explanatory factors were investigated by regression analysis. RESULTS: = 306), on average, 3.8 decayed, 8.6 missing, 4.9 filled teeth, and a care index of 41.5% were observed. Social factors (barriers to care and length of DTES residence), dental hygiene (brushing/flossing), and personal (hepatitis C virus infection/methadone usage) factors contributed to their care index level. CONCLUSION: This is the first time that comprehensive information regarding OH status has been collected from a low-income, inner-city community in Canada. Further investigations in the challenges and needs in accessing dental care may develop solutions for better OH in similar communities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".