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Record W4409172208 · doi:10.1080/03601277.2025.2485810

The impact of COVID-19 on older people experiencing homelessness

2025· article· en· W4409172208 on OpenAlexafffundabout
Morgan Cruz Erisman, Sarah L. Canham, Rachel Weldrick, Atiya Mahmood, Rachelle Patille

Bibliographic record

VenueEducational Gerontology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSimon Fraser UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyGerontologyPandemicMedicineVirologyDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.068
GPT teacher head0.506
Teacher spread0.439 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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