Bibliographic record
Abstract
OBJECTIVE: To determine if psychosocial factors explain the socioeconomic disparities in self-perceived oral health that persist after controlling for oral status variables. METHODS: Data came from the participants in the Canadian Community Health Survey 2003 who were residents in the city of Toronto. Oral health variables included self-rated oral health, a 13-item oral health scale, denture wearing, and having a tooth extracted in the previous year. The last two measures were regarded as proxy indicators of tooth loss. Psychosocial variables included a self-esteem scale, a depression scale, and single items measuring life satisfaction, life stress, and sense of cohesion. Socioeconomic status was assessed using total annual household income. RESULTS: Interviews were completed with 2,754 dentate persons aged 20 years and over. Bivariate analyses confirmed that there were income gradients in self-rated oral health and scores on the oral health scale. Linear regression analyses confirmed that these persisted after controlling for age, gender, denture wearing, and having a tooth extracted in the previous year. In the model predicting self-rated oral health self-esteem, life satisfaction, stress, a sense of cohesion, and depression also contributed to the model, increased its explanatory power, and reduced the strength of but did not eliminate the association between income and self-rated oral health. Broadly, similar results were obtained when the oral health scale score was used as the dependent variable. In both analyses and all models, denture wearing had the strongest and most enduring effect. CONCLUSION: Psychosocial factors partly but do not wholly explain the socioeconomic disparities in self-perceived oral health in this population after controlling for tooth loss and denture wearing. Other variables need to be added to the models to increase their explanatory power.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".