Food insecurity, home ownership and income-related equity in dental care use and access: the case of Canada
Bibliographic record
Abstract
Abstract Background It has been documented that income is a strong determinant of dental care use in Canada, mostly due to the lack of public coverage for dental care. We assess the contributions of food insecurity and home ownership to income-related equity in dental care use and access. We add to the literature by adding these two variables among other socio-economic determinants of equity in dental care use and access to dental care. Evidence on equity in access to and use of dental care in Canada can inform policymaking. Methods We estimate income-related horizontal inequity indexes for the probability of 1) receiving at least one dental visit in the last 12 months; and 2) lack of dental visits during the 3 years before the interview. We conduct the analyses using data from the 2013–2014 Canadian Community Health Survey (CCHS) at the national and regional level. Results There is pro-rich inequity in the probability of visiting a dentist or an orthodontist and in access to dental care in Ontario. Inequities vary across jurisdictions. Housing tenure and food insecurity contribute importantly to both use of and access to dental care, adding information not captured by standard socio-economic determinants. Conclusions Redistributing income may not be enough to reduce inequities. Careful monitoring of equity in dental care is needed together with interventions targeting fragile groups not only in terms of income but also in improving house and food security.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".