Examining the Care Experiences of People Experiencing Homelessness in Kingston
Bibliographic record
Abstract
Background Across Canada, approximately 235,000 individuals face housing instability, and homelessness has been associated with an increased use of emergency department (ED) care. EDs are a safety net for people experiencing homelessness (PEH), when faced with limitations to primary care access, such as limited service hours and locations. PEH have reported negative ED care experiences, including feeling judged, not having health concerns taken seriously, and experiencing stigmatizing behaviour. Prior negative experiences contribute to the avoidance of care services, exacerbating issues for PEH. Objective This study examines the experiences of unhoused populations accessing ED services, to better understand the adversity they face. Methods The study is participatory and cross-sectional, using the sensemaking Spryng.io platform to survey patients at the Kingston Health Sciences Centre EDs. Sensemaking methodology (SM) uses a mixed-methods research approach that collects data through storytelling. This enables individuals to naturally convey complex information and make sense of their experiences. Within the sensemaking survey, individuals who self-identified as a PEH answered an additional subset of questions about their experiences accessing care. Follow-up focus group discussions (FGDs) will take place at St. Vincent de Paul, an organization that supports people in Kingston with resources such as food, clothes, and housing. FGDs will be used to validate the sensemaking results and co-create care improvement strategies. Conclusion By engaging PEH, our results can lead to actionable care improvement strategies. The study is also a foundation for future research collaborating with PEH and community partners, to mitigate negative care experiences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".