Hospital discharge planning for Canadians experiencing homelessness
Bibliographic record
Abstract
Purpose People experiencing homelessness are high-users of hospital care in Canada. To better understand the scope of the issue, and how these patients are discharged from hospital, a national survey of key stakeholders was conducted in 2017. The paper aims to discuss this issue. Design/methodology/approach The Canadian Observatory on Homelessness distributed an online survey to their network of members through e-mail and social media. A sample of 660 stakeholders completed the mixed-methods survey, including those in health care, non-profit, government, law enforcement and academia. Findings Results indicate that hospitals and homelessness sector agencies often struggle to coordinate care. The result is that these patients are usually discharged to the streets or shelters and not into housing or housing with supports. The health care and homelessness sectors in Canada are currently structured in a way that hinders collaborative transfers of patient care. The three primary and inter-related gaps raised by survey participants were: communication, privacy and systems pressures. Research limitations/implications The findings are limited to those who voluntarily completed the survey and may indicate self-selection bias. Results are limited to professional stakeholders and do not reflect patient views. Practical implications Identifying systems gaps from the perspective of those who work within health care and homelessness sectors is important for supporting system reforms. Originality/value This survey was the first to collect nationwide stakeholder data on homelessness and hospital discharge in Canada. The findings help inform policy recommendations for more effective systems alignment within Canada and internationally.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".