Self-reported oral health among a community sample of people experiencing social and health inequities: cross-sectional findings from a study to enhance equity in primary healthcare settings
Bibliographic record
Abstract
OBJECTIVE: To describe the self-reported oral health issues among a community sample of primary care clients experiencing socioeconomic disadvantages. METHODS: As part of a larger mixed-methods, multiple case study evaluating an equity-oriented primary healthcare intervention, we examined the oral health of a sample of 567 people receiving care at four clinics that serve marginalised populations in two Canadian provinces. Data collected included self-rated oral health and experiences accessing and receiving healthcare, standard self-report measures of health and quality of life, and sociodemographic information. RESULTS: The prevalence of self-rated poor oral health was high, with almost half (46.3%) of the participants reporting poor or fair oral health. Significant relationships were observed between poor oral health and vulnerabilities related to mental health, trauma and housing instability. Our findings suggest that the oral health of some Canadian populations may be dramatically worse than what is reported in existing population health surveys. CONCLUSIONS: Our findings reinforce the importance of addressing oral health as part of health equity strategies. The health and oral health issues experienced by this client cohort highlight the need for interdisciplinary, team-based care that can address the intersections among people's health status, oral health and social issues.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".