The role of patient navigation in supporting low-income older adults in their housing needs during hospital to home transitions: A qualitative descriptive study from Ontario, Canada
Bibliographic record
Abstract
Introduction Housing is an important determinant of health. Little research has explored hospital and community agency staff perspectives on how to support the housing needs of low-income older adults. Therefore, this paper examines the challenges associated with supporting low-income seniors as they transition from hospital to home and explores what role, if any, patient navigation models of care could have in addressing housing needs. Methods A thematic secondary analysis that triangulated data from two qualitative studies was used. In total, interviews and/or focus groups with 109 hospital and community care workers were re-analyzed, applying a new interpretive lens to the data to reveal new insights. Data were collected in Ontario, Canada. Results Participants described how low-income older adults have increasing complex care needs that influence their housing, but housing supports are limited and difficult to navigate. Participants believed further support was needed and suggested that a housing-specific patient navigation model of care may be beneficial, but difficult to implement due to the limitations of existing services. Discussion Our findings provide a unique perspective on the challenges hospital and community staff face in caring for older adults with housing needs. Patient navigation with a focus on housing may support these older adults. Further work needs to be undertaken to better understand how to best implement sustainable housing patient navigation models of care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".