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Record W4315865854 · doi:10.14283/jfa.2023.4

Reference Standard for the Measurement of Loss of Autonomy and Functional Capacities in Long-Term Care Facilities

2023· review· en· W4315865854 on OpenAlexaffabout
F. Buckinx, Éva Peyrusqué, Marie Jeanne Kergoat, M. Aubertin-Leheudre

Bibliographic record

VenueThe Journal of Frailty & Aging · 2023
Typereview
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsAutonomyLong-term careGerontologyMedicineActivities of daily livingAssisted livingAssisted Living FacilityOlder peopleIndependence (probability theory)Elderly peopleIndependent livingNursing homesPopulationNursingPhysical therapyEnvironmental health

Abstract

fetched live from OpenAlex

The vast majority of people living in long-term care facilities (LTCFs) are octogenarians (i.e., in Québec, 57.4% of the residents are age 85 or older, 26.2% are between age 75 and 84, 10.7% are between age 65 and 74, and 5.7% are below age 65 (1)), who are affected by a great loss of physical or cognitive autonomy due to illnesses and are unable to maintain their independence, safety and mobility at home. For the majority of them, their last living environment will be a LTCF. Moreover, the annual turnover in LTCFs is one-third of all residents (2) while the average length of stay is 823 days (1). Therefore the main challenges for caregivers in LTCFs are the maintenance of functional capacities and preventing patients from becoming bedridden and isolated. Measuring the level of autonomy and functional capacities is therefore a key element in the care of institutionalized people. Several validated tools are available to quantify the degree of dependence and the functional capacities of older people living in long-term care facilities. This narrative review aims to present the characteristics of the specific population living in long-term care facilities and describe the most widely used and validated tools to measure their level of autonomy and functional capacities.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.756
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.275
GPT teacher head0.436
Teacher spread0.161 · 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 designOther design
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes2
Has abstractyes

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