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Record W4386584654 · doi:10.21900/j.jloe.v3.1190

Familiarity, Autonomy, and Safety Together (FAST): A program for adults with dementia and their caregivers

2023· article· en· W4386584654 on OpenAlexaffabout
James Bachmann

Bibliographic record

VenueJournal of Library Outreach and Engagement · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDementiaAutonomyAssisted livingAssisted Living FacilityGerontologyPsychologyAging in placeActivities of daily livingNursingMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Almost 50 million people worldwide are currently living with dementia, with approximately half a million and six million of those people living in Canada and the United States, respectively. Familiarity, Autonomy, and Safety Together (FAST) is a program idea for adults living with dementia and their family caregivers that draws upon the ideas of familiarity, autonomy, and safety that underlie dementia villages, a recent development in residential care facilities for people living with dementia. FAST uses themed stations that emphasize familiarity and which program participants living with dementia can move about and participate in at will, providing an often lost sense of autonomy. At the same time, caregivers, who often suffer from burnout and related problems, are provided an opportunity to socialize with each other and receive education and support. In this way, people living with dementia and their caregivers can independently benefit from the FAST program. Further, many libraries will be able to provide the FAST program at little or no cost to the library, and a modified version of the FAST program can be brought to patrons in other locations, such as a residential care facility.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.308
Teacher spread0.284 · 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
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

Citations0
Published2023
Admission routes2
Has abstractyes

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