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Record W2899753546 · doi:10.1093/geroni/igy023.2754

NURSING HOMES WITHOUT WALLS: SERVICE MODEL FOR AGING IN PLACE

2018· article· en· W2899753546 on OpenAlexaffabout
Suzanne Dupuis‐Blanchard

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsAging in placeNursingNursing homesService (business)Focus groupAged careDescriptive researchGerontologyQualitative researchCommunity serviceMedicineBusinessSociologyPublic relationsPolitical scienceMarketing

Abstract

fetched live from OpenAlex

In Canada, 92 % of adults over the age of 65 are able to stay in their home independently or with informal and formal supports. However, in recent years, research has shown that access to services to facilitate aging in place is problematic. The goal of this descriptive qualitative study was to identify the needs of older adults aging in place in response to the potential role of nursing homes offering services for aging in place and to identify necessary actions for care/service model development. Focus groups were conducted in four rural communities in the province of New Brunswick with a total of 219 participants. Results indicate that the needs identified by seniors could be remedied by the local nursing home offering services in the community or by inviting community seniors to attend nursing home activities. Nursing home administrators believe they can offer these additional services with appropriate resources.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.077
GPT teacher head0.434
Teacher spread0.357 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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