A ‘snap shot’ of the health of homeless people in inner Sydney: St Vincent’s Hospital
Bibliographic record
Abstract
Objectives. The poor health profile of people who are homeless results in a disproportionate use of health resources by these people. An in-hospital count of demographic and health data of homeless patients was conducted on two occasions at St Vincent’s Hospital in Sydney as an indicator of health resource utilisation for the Sydney region. Methods. Two in-hospital counts were conducted of homeless patients within the boundaries of St Vincent’s Hospital to coincide with the inaugural City of Sydney homeless street counts in winter 2008 and summer 2009. Data collected included level of homelessness, principal diagnosis, triage category, bed occupancy and linkages to services post hospital discharge. Results. Homeless patients at St Vincent’s utilised over four times the number of acute ward beds when compared with the state average. This corresponds to a high burden of mental health, substance use and physical health comorbidities in homeless people. There was high utilisation of mental health and drug and alcohol services by homeless people, and high levels of linkages with these services post-discharge. There were relatively low rates of linkage with general practitioner and ambulatory care services. Conclusion. Increasing knowledge of the health needs of the homeless community will assist in future planning and allocation of health services. What is known about the topic? The poor health status of people who are homeless has been previously noted in the USA, Canada and Scotland. What does this paper add? Homeless people living in Sydney also have a poor health profile and a disproportionate use of health resources when compared to people in the general population. What are the implications for practitioners? Health services for homeless people should be equipped to deal with mental health, substance use and physical health comorbidities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".