High prevalence of unmet healthcare need among people who use illicit drugs in a Canadian setting with publicly-funded interdisciplinary primary care clinics
Bibliographic record
Abstract
Background People who use illicit drugs (PWUD) experience significant barriers to healthcare. However, little is known about levels of attachment to primary care (defined as having a regular family doctor or clinic they feel comfortable with) and its association with unmet healthcare needs in this population. In a Canadian setting that features novel publicly-funded interdisciplinary primary care clinics, we sought to examine the prevalence and correlates (including attachment to primary care) of unmet healthcare needs among PWUD. Methods Data were derived from two prospective cohort studies of PWUD in Vancouver, Canada between December 2017 and November 2018. Multivariable logistic regression was used to identify factors associated with self-reported unmet healthcare needs among participants reporting any health issues. Results In total, 743 (83.6%) of 889 eligible participants reported attachment to primary care and 220 (24.7%) reported an unmet healthcare need. In multivariable analyses, attachment to primary care at an integrated care clinic (adjusted odds ratio [AOR] = 0.14; 95% Confidence Interval [CI]: 0.06–0.34) was negatively associated with an unmet healthcare need, while being treated poorly at a healthcare facility (AOR = 5.50; 95% CI: 3.59–8.60) and self-reported chronic pain (AOR = 2.00, 95% CI: 1.30–3.01) were positively associated with an unmet healthcare need. Conclusion Despite the high level of attachment to primary care, a quarter of our sample reported an unmet healthcare need. Our findings suggest that multi-level interventions are required to address the unmet need, including pain management and integrated care, to support PWUD with complex health needs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".