Assessing the relationship between dental appearance and the potential for discrimination in Ontario, Canada
Bibliographic record
Abstract
Poor oral health is influenced by a variety of individual and structural factors. It disproportionately impacts socially marginalized people, and has implications for how one is perceived by others. This study assesses the degree to which residents of Canada’s most populated province, Ontario, recognize income-related oral health inequalities and the degree to which Ontarians blame the poor for these differences in health, thus providing an indirect assessment of the potential for prejudicial treatment of the poor for having bad teeth. Data were used from a provincially representative survey conducted in Ontario, Canada in 2010 (n=2006). The survey asked participants questions about fifteen specific conditions (e.g. dental decay, heart disease, cancer) for which inequalities have been described in Ontario, and whether participants agreed or disagreed with various statements asserting blame for differences in health between social groups. Binary logistic regression was used to determine whether assertions of blame for differences in health are related to perceptions of oral health conditions. Oral health conditions are more commonly perceived as a problem of the poor when compared to other diseases and conditions. Among those who recognize that oral conditions more commonly affect the poor, particular socioeconomic and demographic characteristics predict the blaming of the poor for these differences in health, including sex, age, education, income, and political voting intention. Social and economic gradients exist in the recognition of, and blame for, oral health conditions among the poor, suggesting a potential for discrimination amongst socially marginalized groups relative to dental appearance. Expanding and improving programs that are targeted at improving the oral and dental health of the poor may create a context that mitigates discrimination.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".