Examining the experiences of vulnerably housed patients visiting Kingston, Ontario’s emergency departments: a qualitative analysis
Bibliographic record
Abstract
INTRODUCTION: Vulnerably housed individuals access emergency departments (EDs) more frequently than the general population. Despite Canada's universal public health care system, vulnerably housed persons face structural barriers to care and experience discrimination from healthcare providers. This study examines how vulnerably housed persons perceive their experience of care in the ED and Urgent Care Center (UCC) in Kingston, Ontario and aims to develop strategies for improving care for this group. METHODS: As part of a larger mixed-methods study, narratives were collected from participants attending the ED/UCC as well as community-based partner organizations, asking them to describe an experience of a recent ED visit (< 24 months). Participants could identify as members of up to three equity-deserving groups (EDGs) (for example homeless, part of an ethnic minority, having a disability, experiencing mental health issues). Coding and thematic analysis were completed for the experiences of participants who identified as being vulnerably housed (n = 171). Results were presented back to individuals with lived experience and service providers working with clients with unstable housing. RESULTS: Participants reported judgement related to a past or presumed history of mental health or substance use and based on physical appearance. They also often felt unheard and that they were treated as less than human by healthcare providers. Lack of effective communication about the ED process, wait times, diagnosis, and treatment led to negative care experiences. Participants reported positive experiences when their autonomy in care-decision making was respected. Furthermore, having a patient-centered approach to care and addressing specific patient needs, identities and priorities led to positive care experiences. CONCLUSIONS: The ED care experiences of vulnerably housed persons may be improved through healthcare provider training related to trauma-informed and patient-centered care and communication strategies in the ED. Another potential strategy to improve care is to have advocates accompany vulnerably housed persons to the ED. Finally, improving access to primary care may lead to reduced ED visits and better longitudinal care for vulnerably housed persons.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".