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Record W2097827243 · doi:10.1017/s1041610203009608

Cognitively Impaired Older Adults: Risk Profiles for Institutionalization

2003· article· en· W2097827243 on OpenAlexaffabout
Laurel A. Strain, Audrey A. Blandford, Lori Mitchell, Pamela Hawranik

Bibliographic record

VenueInternational Psychogeriatrics · 2003
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of WinnipegUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsInstitutionalisationDementiaGerontologyActivities of daily livingCognitionCognitive impairmentMedicineCaregiver burdenPsychologyClinical psychologyPsychiatryDisease

Abstract

fetched live from OpenAlex

BACKGROUND: This study focused on the identification of risk profiles for institutionalization among older adults diagnosed with cognitive impairment-not dementia or dementia in 1991/92 and subsequent institutionalization in the following 5-year period. METHODS: Data were from a sample of 123 individuals aged 65+ and their unpaid caregivers in Manitoba, Canada. Cluster analysis was conducted using baseline characteristics of age, cognition, disruptive behaviors, ADLs/IADLs, use of formal in-home services, and level of caregiver burden. RESULTS: Three distinct groups emerged (high risk [n = 12], medium risk [n = 40], and low risk [n = 71]). The high-risk group had the poorest cognitive scores, were the most likely to exhibit disruptive behaviors, were the most likely to need assistance with ADLs and IADLs, and had the highest level of burden among their caregivers. Follow-up of the groups validated the risk profiles; 75% of the high-risk group were institutionalized within the next 5 years, compared to 45% of the medium-risk group and 21% of the low-risk group. DISCUSSION: The risk profiles highlight the diversity among individuals with cognitive impairment and the opportunity for differential targeting of services for the distinct needs of each group.

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)0.000
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 score0.210
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.332
Teacher spread0.317 · 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

Citations15
Published2003
Admission routes2
Has abstractyes

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