Emergency care for all? Revealing the barriers and influences felt by homeless people seeking emergency department care and admission: a scoping review
Bibliographic record
Abstract
The health of Individuals Experiencing Homelessness (IEH) is substantially poorer than the general population (Canadian Observatory on Homeless [COH], 2021; Fazel et al., 2014; Magwood et al., 2019.). Homelessness increases the likelihood of multimorbidity and of developing severe illness requiring hospital-based care. Despite this, IEH can be reluctant to access hospital-based services (Magwood et al., 2019) due to systemic barriers and a perception that healthcare services are not designed for them (Perkin et al., 2023). For IEH with healthcare needs exceeding the capabilities of primary care, the barriers and facilitators to accessing emergency hospital-based care must be identified. IEH have the right to dignified healthcare in the setting that will appropriately address their health needs. Using the methodological framework of Arksey and O’Malley (Arksey and O’Malley , 2002), this scoping review aims to identify existing literature, and literature gaps, regarding the factors that influence individuals experiencing homelessness (IEH) to seek emergency department care and inpatient hospital-based care. Additionally, it will explore factors that lead IEH to leave hospital-based care against medical advice (AMA).
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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".