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Record W4294488586 · doi:10.5770/cgj.25.551

Exploring Harm Reduction in Supportive Housing for Formerly Homeless Older Adults

2022· article· en· W4294488586 on OpenAlexafffundvenueabout
Lara Nixon, Victoria Burns

Bibliographic record

VenueCanadian Geriatrics Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Calgary
FundersCumming School of Medicine, University of Calgary
KeywordsHarm reductionSupportive housingVulnerability (computing)MedicineHarmQualitative researchNursingHealth careGerontologyPublic healthPsychologySociologyEconomic growthSocial psychology

Abstract

fetched live from OpenAlex

Background: Exclusionary care policy contributes to the growing number of older adults experiencing homelessness and complex health challenges including substance misuse. The aim of this study was to examine how harm reduction policy and practices are experienced and enacted for older adults with homeless histories and care staff in congregate supportive housing. Methods: Drawing on harm reduction (HR) principles, Rhodes' risk environment framework, and 15 semi-structured interviews (six residents, nine staff) at a 70-bed supportive housing facility in Western Canada, this qualitative constructivist grounded theory study aimed to determine: How is harm reduction experienced and enacted from the perspectives of older adults and their care staff? Results: HR policy and practices helped residents to feel respected and a sense of belonging, due largely to staff's understanding of structural vulnerability related to homelessness and their efforts to earn and maintain residents' trust. Physical and program structures in the facility combined with the social environment to mitigate harms due to substance- and nonsubstance-related risk behaviours. Conclusion: HR policy and practices in supportive living empower care providers and older adults to work together to improve housing and health stability. Wider adoption of HR approaches is needed to meet the needs of a growing number of older people experiencing homelessness and substance use challenges.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
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 head0.100
GPT teacher head0.361
Teacher spread0.261 · 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

Citations13
Published2022
Admission routes4
Has abstractyes

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