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Record W2727621372 · doi:10.1093/geroni/igx004.395

COPE CT FROM ORGANIZATIONAL AND PUBLIC POLICY PERSPECTIVES

2017· article· en· W2727621372 on OpenAlexaboutno aff
S.T. Molony

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidLong-term careIntervention (counseling)DementiaQuarter (Canadian coin)BusinessNursingGerontologyMedicineHealth careEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Home and community-based services (HCBS) offer a lower-cost option than institutional long-term care, while supporting older adults in their preferred setting. Nearly 70 percent of people with dementia live at home (Alzheimer’s Association, 2009) and almost one-quarter has Medicaid coverage (Kaiser Family Foundation, 2015). HCBS services vary from state to state and while many provide options for long-term services and supports (LTSS) such as personal care, case management, physical or occupational therapy, few of these services are designed to formally address the needs of family caregivers essential to successful community living for persons with dementia. The COPE intervention offers a unique constellation of supports to these families that can be integrated into existing LTSS care management models. This paper compares and contrasts “usual care” with the enhanced supports offered as part of the COPE intervention and identifies key points for successful implementation by care managers and HCBS policymakers.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0120.005
Open science0.0010.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0360.002

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 head0.053
GPT teacher head0.411
Teacher spread0.358 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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