The impact of COVID-19 on older people experiencing homelessness
Bibliographic record
Abstract
As a unique population with distinct needs, older people experiencing homelessness (OPEH) have confronted significant challenges since the onset of the COVID-19 pandemic. However, there is limited understanding of how OPEH were affected by the pandemic. Given the vulnerabilities of this population and the paucity of existing research on this topic, we examined how OPEH staying in a temporary housing program in Vancouver, BC were impacted by COVID-19. In-depth semi-structured interviews were conducted (over three sessions) with 11 adults aged 50+ years. We conducted thematic analysis and organized findings into four themes: 1) Fear and contraction of the coronavirus; 2) Reduced access to health and social services; 3) Increased social isolation and reduced social engagement; and 4) Changed social gathering spaces. Findings offer lessons for how providers and policymakers can prepare for future challenges and reduce disparities associated with the pandemic, while highlighting the need to support the unique needs of OPEH and others staying in temporary residences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".