Urban homelessness and emergency department usage: Predictors and user narratives of emergency care
Bibliographic record
Abstract
Emergency Department (ED) usage among people who are homeless is higher than in the general population; however, myths regarding people who are homeless inappropriately using the ED are present in public and scholarly discourse. Further, minimal research has investigated ED use among those who are homeless in a Canadian context, or regarding how those who are homeless understand the role of the ED in their healthcare. Study 1 explores the question of which factors predict ED use among people who are homeless in a Canadian sample. Participants (n = 483) from a local, longitudinal Housing First demonstration project consented to the linkage of their survey responses regarding housing, health and social service use to the provincial administrative health data repository. Predictor relationships were analyzed using negative binomial longitudinal mixed modelling. In the full model ED visits were positively and reliably predicted by Indigenous ancestry, high needs mental illness, pre-baseline ED use, and concurrent increased social assistance, primary care visits, ratings of physical health, substance use problems and case management visits. Study 2 addresses the question of how participants understand the role of the ED in their healthcare and day-to-day lives. A subset of participants from Study 1 were recruited (n = 16) to participate in semi-structured interviews regarding their ED stories and experiences. Interviews were analyzed using narrative analysis. Set within the context of narratives of disempowerment, participants storied the ED in differing ways. The findings indicate that participants understand the ED to be a public, accessible space where they could exert agency in obtaining necessary healthcare. ED narratives were also paradoxical, storying it as a fixed place of transient care in their transient lives; as a result, they were isolated, and yet belonged. Each study is accompanied by a discussion of the implications of their respective findings. The thesis includes a synthesis of the findings from the quantitative and qualitative studies. Overall, the findings from the combined research challenge misconceptions about the inappropriateness of ED use among people who are homeless and call for a cessation of propagating societal narratives that risk compromising the quality of their healthcare.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".