Predictors of Emergency Department Use among Individuals with Current or Previous Experience of Homelessness
Bibliographic record
Abstract
This study assessed the contributions of predisposing, enabling, and needs factors in predicting emergency department (ED) use among 270 individuals with current or previous experience of homelessness. Participants were recruited from three different types of housing (shelter, temporary housing and permanent housing) in Montreal, Quebec (Canada). They were interviewed at baseline (T0), and again 12 months after recruitment (T1). Longitudinal data analyses were conducted on associations between a set of baseline predictors (T0) with the dependent variable (ED users vs. non-users) from T1. Predictors were identified according to the Gelberg-Andersen Behavioral Model. Findings revealed two needs factors associated with ED use: having a substance use disorder (SUD) and low perceived physical health. Two enabling factors, use of ambulatory specialized services and stigma, were also related to ED use. No predisposing factors were retained in the model, and ED use was not associated with type of housing. Improvements are needed in SUD and physical health management in order to reduce ED use, as well as interventions aimed at stigma prevention for this vulnerable population.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".