Implementation and evaluation of a novel community-based urban mobile health clinic in Toronto, Ontario
Bibliographic record
Abstract
SETTING: Despite Canada's single-payer health system, marginalized populations often experience poor health outcomes and barriers to healthcare access. In response, mobile health clinics (MHCs) have been deployed in several cities across Canada. MHCs are well established in the United States; however, little is known about their role and impact in a country with universal healthcare. We describe the implementation of an urban MHC and early learnings from a mixed methods process and outcome-oriented evaluation. INTERVENTION: In February 2021, Parkdale Queen West Community Health Centre, TELUS Health for Good, and University Health Network's Gattuso Centre for Social Medicine partnered to launch a nurse practitioner‒led, community-based MHC in Toronto, Ontario. The MHC provides low-barrier primary healthcare, harm reduction, and mental health services at five convenient locations. OUTCOMES: Through an intercept survey (n = 49) and semi-structured interviews (n = 10), we sought to understand the sociodemographic characteristics of clients, their experiences at the MHC, and barriers and facilitators to the MHC in comparison to traditional healthcare settings. Most clients surveyed reported being homeless (61%). Without the MHC, 37% of clients would have accessed care at an emergency department and 18% would not have sought care. Thematic analysis revealed two structural and two relational factors that improved care experiences and care access. IMPLICATIONS: We demonstrate that in a single-payer health system, MHCs alleviate major barriers to care access for marginalized populations. Learnings provide context to the most salient factors influencing clients' decisions to seek care at MHCs and can inform how these outreach models are designed.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".