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Record W4404752249 · doi:10.1093/haschl/qxae154

Older adults in social housing: A systemically vulnerable population that needs to be prioritized

2024· article· en· W4404752249 on OpenAlexafffundabout
Jasmine Dzerounian, Guneet Mahal, Leena AlShenaiber, Ricardo Angeles, Francine Marzanek, Melissa Pirrie, Gina Agarwal

Bibliographic record

VenueHealth Affairs Scholar · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcMaster UniversityImpact
FundersHealth CanadaGovernment of Canada
KeywordsPopulationGerontologyPsychological interventionHealth careSocial supportHealth literacyEnvironmental healthPopulation ageingMedicineBusinessPsychologyEconomic growthNursingEconomics

Abstract

fetched live from OpenAlex

Older adults living in social housing are a vulnerable population with unique health challenges that often lead to poor health outcomes and high emergency service utilization. However, the needs of this population are frequently overlooked. This policy note describes the characteristics of older adults living in social housing in Canada and discusses why they are a vulnerable, underserved population in need of immediate attention and priority. Older adults in social housing have higher rates of chronic disease, lower quality of life, and lower health literacy and face challenges caused by various compounding social determinants of health. There is a large gap in research and tailored interventions focusing on this population. Based on these findings, the authors highlight the need for the allocation of resources to support this growing population, including dedicated funding, research, and programming. Proactively addressing the issues that exist in the health and social care of this high-needs population will also have larger implications for reducing healthcare system burden.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.361
Teacher spread0.324 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations4
Published2024
Admission routes3
Has abstractyes

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