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Record W3194552692 · doi:10.5014/ajot.2021.044735

Unpaid Caregiving and Aging in Place in the United States: Advancing the Value of Occupational Therapy

2021· article· en· W3194552692 on OpenAlexaff
Beth Fields, Juleen Rodakowski, Vanessa Jewell, Sajay Arthanat, Melissa Park, Catherine Verrier Piersol, Stacey L. Schepens Niemiec, Jennifer L. Womack, Tracy M. Mroz

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychosocialOccupational therapyValue (mathematics)Health carePsychologyGerontologyNursingMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Unpaid caregivers are often expected to help family members or friends overcome activity limitations and participation restrictions to successfully age in place. Caregivers assume multiple responsibilities, such as managing their own physical and psychosocial needs and navigating a complex health care system, and many feel ill equipped to fulfill the necessary health care responsibilities for their care recipients. Underprepared caregivers may cause poor outcomes for care recipients. Federal and state policy proposals call attention to the need to better support caregivers, especially as their numbers increase. Occupational therapy practitioners are well positioned to effectively engage caregivers as they navigate the health care system. The occupational therapy process looks broadly at the functional abilities, environmental contexts, and occupational demands that play a pivotal role in successful aging in place for clients and better outcomes for their caregivers. Now is the time to define occupational therapy's distinct value to this area.

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)0.005
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 score0.025
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.138
GPT teacher head0.495
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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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