Implementing a hospital-based case management intervention for people experiencing homelessness: the navigator program
Bibliographic record
Abstract
BACKGROUND: People experiencing homelessness (PEH) have worse health than the general population, and higher rates of hospitalization. The transition period after discharge from hospital is often challenging for PEH, in part due to loss to follow-up, competing priorities, housing instability, and the absence of a primary care provider. In-patient hospital stays represent a window of opportunity to intervene and connect with patients, supporting them to stay in hospital and complete their treatment plan, identify and address their social needs, and support their transition of care into the community. This qualitative study explores supports and challenges to the implementation of the Navigator Program, a hospital-based critical time intervention that supports PEH during their hospital stay and after discharge into the community. METHODS: We interviewed 35 participants (homeless outreach counsellors working on the program, hospital physicians and staff in the implementation setting, community service providers, and the implementation team) and conducted 130 h of non-participant observation. Analysis used the Framework Method and the Consolidated Framework for Implementation Research to highlight the barriers and facilitators to implementation. RESULTS: A core aspect of successful implementation and program uptake was that all participants saw a need for the program. The flexible approach to model design and implementation was an essential approach to program development that adjusted to the implementation setting, while leaving room to create more systems and structures as the program progresses. Implementation also relied on clear approaches to attaining buy-in from all stakeholders, done through a mix of formal and informal approaches. Operating as a hospital-based program was essential for successful implementation, supporting team-building among care providers in both the healthcare and social service sectors, which can lead to improved patient care coordination. CONCLUSION: The implementation of programs addressing complex social and health issues can contribute to its success or failure. In this study, we discuss the effective implementation approaches of the Navigator Program, as well as lessons learned. This study provides practical and helpful strategies for implementing similar programs in hospitals across Canada, and in countries with similar healthcare system structures.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".