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Record W2899534397 · doi:10.1093/geroni/igy023.1396

SENIOR SERVICES THAT SUPPORT HOUSING FIRST IN METRO VANCOUVER, CANADA

2018· article· en· W2899534397 on OpenAlexaffabout
Sarah L. Canham, Lupin Battersby, Mei Lan Fang, Mineko Wada, R.M.R. Barnes, Andrew Sixsmith

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsFraser InstituteSimon Fraser University
Fundersnot available
KeywordsAffordable housingCitizen journalismService providerService (business)BusinessOrder (exchange)Housing FirstAging in placeSupportive housingParticipatory action researchPublic relationsEconomic growthNursingGerontologyPolitical scienceMedicineMarketingFinanceMental healthEconomics

Abstract

fetched live from OpenAlex

In order to implement and deliver Housing First, a model and philosophy for housing homeless people in immediate and permanent housing with individualized supports, research is needed on the system of support services as they currently exist. Guided by principles of community-based participatory research, this paper presents findings from a senior-focused deliberative dialogue workshop in Vancouver, Canada. Participants (16 service providers and 1 service recipient) identified services and resources available to support seniors in maintaining housing and barriers and facilitators for accessing services. Data suggest that affordable housing that adapts to changing health conditions, income supports, health services, homecare, transportation, and culturally appropriate and nondiscriminatory informational resources are among the supports needed older adults to succeed under the Housing First model. Barriers to Housing First service provision, including eligibility criteria, should be reconsidered in light of the growing number of older adults who are newly experiencing homelessness.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.033
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.001
Scholarly communication0.0020.000
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.376
Teacher spread0.334 · 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
Published2018
Admission routes2
Has abstractyes

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