Evaluation of a hospital-based case management programme for patients who are experiencing homelessness: A qualitative study of health care workers’ experiences with the Navigator Programme
Bibliographic record
Abstract
Introduction: People experiencing homelessness (PEH) have poorer health outcomes after hospital discharge compared with housed individuals. The Navigator Programme is a hospital-based case management programme for PEH that connects Homeless Outreach Counsellors (HOCs) with patients during their hospital admission and follows them in the community for approximately 90 days after hospital discharge. This qualitative study explores the experiences of health care workers who provided care for PEH during their hospitalization and who interacted with the Navigator Programme. Methods: As part of a larger process evaluation, we conducted in-depth, semi-structured interviews with health care workers using an iterative interview guide. We employed thematic analysis, using both a deductive and inductive approach. Results: We interviewed 14 participants at a single academic teaching hospital where the Navigator Programme had been implemented. Study participants identified several benefits of having the Navigator Programme embedded in the hospital, including ease of referral, rapid contact between programme staff and patients, high-quality communication with programme staff, and direct observation of the programme's benefits for patients. The programme helped alleviate participants’ moral distress that was triggered by the unmet needs of PEH. However, participants leveraged the Navigator Programme to offload tasks, rather than to support them in accomplishing tasks. Conclusion: Our findings highlight that a hospital-based case management programme is an innovative approach that can address the discontinuity between hospitals and community-based programmes. This programme also addresses the moral injury experienced by hospital-based workers caring for PEH. Role clarification is an important future consideration.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".