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Record W2730149591 · doi:10.1093/geroni/igx004.3384

NATURE AND MEANING OF SOCIAL TIES AMONG ASSISTED LIVING RESIDENTS AT THE END OF LIFE

2017· article· en· W2730149591 on OpenAlexaboutno aff
Molly M. Perkins, Sean N. Halpin, María Rosa Salvador Comino, Mary M. Ball, Candace L. Kemp, Patrick J. Doyle, Ann E. Vandenberg, Tammie E. Quest

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)FeelingPsychologySocial isolationGerontologyCognitive impairmentThematic analysisCognitionQuality of life (healthcare)Interpersonal tiesReminiscenceSocial supportSocial psychologyDevelopmental psychologyClinical psychologySociologyMedicineQualitative researchPsychiatryPsychotherapistCognitive psychologySocial science

Abstract

fetched live from OpenAlex

Social network size and quality of perceived social support are associated with overall well-being in late life. Our research on end-of-life in assisted living (AL) shows that size of residents’ social support networks tends to decrease as cognitive impairment increases. Using Year 1 data from a 5-Year NIA-funded study (1R01AG047408-01A1) focusing on 55 residents with cognitive impairment from four diverse AL communities, we investigate the nature and meaning of these shrinking networks. Residents’ mean age is 87 (range=71–103); 47% are African American and 62% are female. The Montreal Cognitive Assessment shows that more than half (65%) have moderate-to-severe cognitive impairment. Thematic analysis shows that some residents draw strength through reminiscence and spiritual ties they maintain with deceased family and friends. As “comrades in illness,” others report feelings of isolation related to a lack of meaningful interaction with co-residents. Findings have implications for strengthening social support at end-of-life in AL.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.394
Teacher spread0.348 · 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.

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
Published2017
Admission routes1
Has abstractyes

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