A paradox of need: Gaps in access to dental care among people who use drugs in Canada's publicly funded healthcare system
Bibliographic record
Abstract
In Canada, publicly funded healthcare provides no-cost access to a large but not comprehensive suite of services. Dental care is largely funded by private insurance or patients, creating employment- and income-dependent gaps in care access. Difficulties accessing dental care may be amplified among vulnerable populations, including people who use drugs (PWUD), who may experience greater dental need due to side effects of substance use and health comorbidities, as well as barriers to care. Using data collected between 2014 and 2018 from two ongoing prospective cohort studies of PWUD in Vancouver, Canada, the aim of this study was to explore factors associated with dental care access. Among 1,638 participants, 246 participants (15%) reported never or only occasionally accessing adequate dental care. In generalised linear mixed-effects models, results showed significant negative associations between accessing dental care and using opioids (Adjusted Odds Ratios [AOR] = 0.73, 95% Confidence Interval [CI] = 0.58-0.91), methamphetamine (AOR = 0.75, 95% CI = 0.59-0.95) and cannabis (AOR = 0.78, 95% CI = 0.63-0.97), as well experiencing homelessness (AOR = 0.54, 95% CI = 0.42-0.70) and street-based income generation (AOR = 0.75, 95% CI = 0.59-0.94). There were significant positive associations between adequate dental care and accessing opioid agonist treatment (OAT) for opioid dependence (AOR = 1.36, 95% CI = 1.07-1.72) and receiving income assistance (AOR = 1.70, 95% CI = 1.05-2.77). These results highlight specific substance use patterns and structural exposures that may hinder dental care access, as well as how direct and indirect benefits of income assistance and OAT may improve access. These findings provide support for recent calls to expand healthcare coverage and address dental care inequities.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".