“Like an umbrella, protecting me from the rain until I get to my destination”: Evaluating the implementation of a tailored primary care model for urban marginalized populations
Bibliographic record
Abstract
Abstract Background Improving health equity and access to the highest possible standard of health care is a key issue of social accountability. Centretown Community Health Centre in Ottawa, Canada has iteratively developed a program to target and serve marginalized and complex populations since 1999. The program implementation was evaluated using a validated implementation framework. Methods Quantitative and qualitative data were collected through a health records extraction (n = 570), a client complexity assessment tool (n = 74), semi-structured interviews with clients and key stakeholders (n = 41), and a structured client satisfaction survey (n = 30). Data were analyzed using descriptive statistics and inductive thematic analysis. Results Five hundred and seventy unique clients were seen between November 1–30, 2021. A third of clients (34%) did not have a provincial health card for access to universal health care services, and most (68%) were homeless or a resident of rooming houses. Most clients who reported their income (92%) were at or below Canada’s official poverty line. The total mean complexity score for clients seen over a one-month period (n = 74) was 16.68 (SD 6.75) where a total score of at least 13 of 33 is perceived to be a threshold for client biopsychosocial complexity. Clients gained the majority of their total score from the Social support assessment component of the tool. Clients (n = 31) and key informants (n = 10) highlighted the importance of building relationships with this population, providing wrap-around care, and providing low-barrier care as major strength to the Urban Health program (UH). Key areas for improvement included the need to: i) increase staff diversity, ii) expand program hours and availability, and iii) improve access to harm reduction services. Clients appeared to be highly satisfied with the program, rating the program an average total score of 18.50 out of 20. Conclusions The program appears to serve marginalized and complex clients and seems well-received by the community. Our findings have relevance for other health care organizations seeking to better serve marginalized and medically and socially complex individuals and families in their communities.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 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".