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Record W1578086507 · doi:10.1300/j031v18n02_05

The Supportive Community

2006· article· en· W1578086507 on OpenAlexaboutno aff
Ayelet Berg‐Warman, Jenny Brodsky

Bibliographic record

VenueJournal of Aging & Social Policy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorVariety (cybernetics)Aging in placeQuarter (Canadian coin)BusinessPublic relationsNursingPsychologyMedicineGerontologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This study examines supportive community programs in Israel, which aim to improve the quality of life of the elderly. These innovative programs pool existing resources to provide a benefits package that includes medical services, an emergency call switchboard, a “neighborhood facilitator,” and social activities. Data were collected in 2000-2001 using qualitative and quantitative methods. The program provides specific services to meet needs that otherwise are not adequately addressed. The major contributions of the program reported by the members was increasing their personal security (two-thirds), easing the burden on their children (one-third), and enabling them to remain at home (one-quarter). The supportive community program enriches the variety of services available, thus providing the elderly with the choice of staying within their familiar surroundings of their homes and neighborhoods. This model appears to be both a cost-effective way to facilitate aging in place and a way to meet many of the elderly's essential needs, thereby maintaining their quality of life.

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)0.002
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.046
GPT teacher head0.444
Teacher spread0.398 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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