A comparison of the COVID-19 response for urban underserved patients experiencing healthcare transitions in three Canadian cities
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic and response has highlighted existing strengths within the system of care for urban underserved populations, but also many fault lines, in particular during care transitions. The objectives of this study were to describe COVID-19 response policies for urban underserved populations in three Canadian cities; examine how these policies impact continuity of care for urban underserved populations; determine whether and how urban underserved community members were engaged in policy processes; and develop policy and operational recommendations for optimizing continuity of care for urban underserved populations during public health crises. METHODS: Using Walt & Gilson's Policy Triangle framework as a conceptual guide, 237 policy and media documents were retrieved. Five complementary virtual group interview sessions were held with 22 front-line and lived-experience key informants to capture less well-documented policy responses and experiences. Documents and interview transcripts were analyzed inductively for policy content, context, actors, and processes involved in the pandemic response. RESULTS: Available documents suggest little focus on care continuity for urban underserved populations during the pandemic, despite public health measures having disproportionately negative impacts on their care. Policy responses were largely reactive and temporary, and community members were rarely involved. However, a number of community-based initiatives were developed in response to policy gaps. Promising practices emerged, including examples of new multi-level and multi-sector collaboration. CONCLUSION: The pandemic response has exposed inequities for urban underserved populations experiencing care transitions; however, it has also exposed system strengths and opportunities for improvement to inform future policy direction.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".