Recency of immigration and utilization of dental care services in Canada
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between recency of immigration to Canada and the utilization of dental health services. METHODS: The cross-sectional study sample (n = 2137) was drawn from the 2015-2016 Canadian Community Health Survey (CCHS). It consisted of Canadian residents aged 12 years and older who resided in the two provinces and one territory who opted into the optional dental module and gave valid responses to the questions 'How often do you usually see a dental professional, such as a dentist, a dental hygienist or a denturologist?' and 'Length of time since immigration to Canada?' for the outcome and independent variable, respectively. Multinomial logistic regression was used to analyse the data, and all statistics were weighted using sampling weights provided by Statistics Canada. RESULTS: The adjusted odds ratios were lower for recent immigrants than for established immigrants and for visits more than once per year (OR = 0.35; 95% CI 0.14, 0.92), about once per year (OR = 0.34; 95% CI 0.13, 0.90) and for less than once per year (OR = 0.22; 95% CI 0.07, 0.64) than for those who never visited a dental professional. Recent immigrants, males, individuals aged 70 years or more and those with a low household income were less likely to visit a dental professional than established immigrants, females, younger age groups or those with higher incomes. CONCLUSION: Better policies are needed to address the dental health concerns of recent immigrants who may suffer from poorer dental health, to ensure that they receive the care they require.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".