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Record W3080700794 · doi:10.1017/s0714980820000045

Push and Pull Factors Surrounding Older Adults’ Relocation to Supportive Housing: A Scoping Review

2020· review· en· W3080700794 on OpenAlexafffundabout
Bryan B. Franco, Jason Randle, Lauren Crutchlow, Janet Heng, Arsalan Afzal, George Heckman, Véronique Boscart

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2020
Typereview
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsConestoga CollegeUniversity of WaterlooResearch Institute for AgingQueen's University
FundersResearch Institute for Aging, University of WaterlooNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Health and Long-Term Care
KeywordsRelocationAging in placeSupportive housingGerontologyPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

Supportive housing, including retirement homes and assisted living, is increasingly touted as a suitable living option for Canadian older adults. This scoping review describes the nature and content of studies that explore underlying factors that motivate older adults to relocate to supportive housing. We conducted a search of PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Web of Science, and PsycINFO, which identified 34 articles for review. Articles reviewed employed a variety of methods and guiding theoretical frameworks, of which the push and pull framework appeared to be most common. This review suggests that health and functional deficits are important reasons for relocation to supportive housing for older adults. Further longitudinal data are required to more comprehensively describe medical and social determinants for relocation and its consequences, in order to better describe this growing population and better align policies with the needs of older adults contemplating or undergoing relocation.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.030
GPT teacher head0.301
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207