Oral health disparities and food insecurity in working poor Canadians
Bibliographic record
Abstract
OBJECTIVES: This study explored oral health disparities associated with food insecurity in working poor Canadians. METHODS: We used a cross-sectional stratified study design and telephone survey methodology to obtain data from 1049 working poor persons aged between 18 and 64 years. The survey instrument contained sociodemographic items, self-reported oral health measures, access to dental care indicators (dental visiting behaviour and insurance coverage) and questions about competing financial demands. Food-insecure persons gave 'often' or 'sometimes' responses to any of the three food insecurity indicators used in the Canadian Community Health Survey (2003) assessing 'worry' about not having enough food, not eating enough food and not having the desired quality of food because of insufficient finances in the previous 12 months. RESULTS: Food-insecure working poor persons had poor oral health compared with food-secure working poor persons indicated by a higher percentage of denture wearers (P < 0.001) and a higher prevalence of toothache, pain and functional impacts related to chewing, speaking, sleeping and work difficulties (P < 0.001). Fewer food-insecure persons rated their oral health as good or very good (P < 0.001). Logistic regression analyses showed that oral health disparities between food-insecure and food-secure persons related to denture wearing, having a toothache, reporting poor/very poor self-rated oral health or experiencing an oral health impact persisted after adjusting for sociodemographic factors and access to dental care factors (P < 0.05). Food-insecure working poor persons reported relinquishing goods or services in order to pay for necessary dental care. CONCLUSIONS: This study identified oral health disparities within an already marginalized group not alleviated by access to professional dental care. Working poor persons regarded professional dental care as a competing financial demand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 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".