Cardiovascular disease management and healthcare delivery for people experiencing homelessness: a scoping review
Bibliographic record
Abstract
Abstract Background People experiencing homelessness have increased prevalence, morbidity, and mortality of cardiovascular disease (CVD), attributable to several traditional and non-traditional risk factors. While this burden is well-known, mainstream CVD management plans and healthcare delivery have not been developed with people experiencing homelessness in mind nor tailored to their unique context. The overall objective of this work was to explore and synthesize what is known about CVD management experiences, programs, interventions, and/or recommendations specifically for people experiencing homelessness. Methods We conducted a scoping review to combine qualitative and quantitative studies in a single review using the Arksey and O’Malley framework and lived experience participation. We performed a comprehensive search of OVID Medline, Embase, PsychINFO, CINAHL, Web of Science, Social Sciences Index, Cochrane, and the grey literature with key search terms for homelessness, cardiovascular disease, and programs. All dates, geographic locations, and study designs were included. Articles were analyzed using conventional content analysis. Results We included 37 articles in this review. Most of the work was done in the USA. We synthesized articles’ findings into 1) barriers/challenges faced by people experiencing homelessness and their providers with CVD management and care delivery (competing priorities, lifestyle challenges, medication adherence, access to care, and discrimination), 2) seven international programs/interventions that have been developed for people experiencing homelessness and CVD management with learnings, and 3) practical recommendations and possible solutions at the patient encounter level (relationships, appointment priorities, lifestyle, medication), clinic organization level (scheduling, location, equipment, and multi-disciplinary partnership), and systems level (root cause of homelessness, and cultural safety). Conclusions There is no ‘one-size-fits all’ approach to CVD management for people experiencing homelessness, and it is met with complexity, diversity, and intersectionality based on various contexts. It is clear, however, we need to move to more practically-implemented, community-driven solutions with lived experience and community partnership at the core. Future work includes tackling the root cause of homelessness with affordable housing, exploring ways to bring cardiac specialist care to the community, and investigating the role of digital technology as an avenue for CVD management in the homeless community. We hope this review is valuable in providing knowledge gaps and future direction for health care providers, health services research teams, and community organizations.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".