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Record W4212902312 · doi:10.55365/1923.x2021.19.16

Detecting the Incidence and Benefits Foregone of Elder Abuse Neglect: The Case of Hearing Aids in Nursing Homes

2021· article· en· W4212902312 on OpenAlexvenueno aff
Robert J. Brent

Bibliographic record

VenueReview of Economics and Finance · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of Health
KeywordsNeglectElder abuseContext (archaeology)Test (biology)Nursing homesIncidence (geometry)Hearing lossMedicineNursingPsychologyGerontologyPsychiatryPoison controlSuicide preventionMedical emergencyAudiologyHistory

Abstract

fetched live from OpenAlex

Neglect is the form of elder abuse that is most likely to be underreported. We provide a three-part test that can be used to uncover the incidence of elder abuse neglect. We apply the test to nursing homes in the form of the under-provision of hearing aids (HAs). To prove HA neglect, it must be shown that they are needed, socially worthwhile and underprovided. All three test results reveal negligence in the context of HAs in nursing homes. The main contribution of the article is to provide empirical evidence related to the third test, based on a large national panel data set using a two-way, random effects regression. Nursing home usage of HAs is one-sixth lower that it would be if older adults lived out in the community. The approximate value lost in the US by this elder abuse neglect is estimated to be $4.4 billion. It is recommended that greater use of hearing aids would be forthcoming if the nursing home institutions themselves recognized the nature and scope of the hearing loss problem of residents

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)0.001
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 score0.932
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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
Published2021
Admission routes1
Has abstractyes

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