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Record W1997750487 · doi:10.1080/13571516.2011.584431

Labor Prices and the Treatment of Nursing Home Residents with Dementia

2011· article· en· W1997750487 on OpenAlexaff
David C. Grabowski, John R. Bowblis, Judith A. Lucas, Stephen Crystal

Bibliographic record

VenueInternational Journal of the Economics of Business · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsDementiaNursing homesNursingMedicineAntipsychoticBusinessPsychiatrySchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

An important aspect of dementia care for nursing home residents is the management of symptoms such as behavioral problems and wandering. Nursing homes can manage these symptoms using a mix of labor, medications and physical restraints. Medications such as antipsychotics are hypothesized to be a substitute for direct care staff, while physical restraints are considered to be a complement to staff time. Using an instrumental variables approach, we investigate whether an increase in nursing home wages leads to greater substitution towards antipsychotic medications and away from the use of physical restraints. Our results suggest a 10% increase in weekly nursing home wages increases the inappropriate use of antipsychotics among dementia patients by 1.1% to 3.5%, while it decreases the use of physical restraints by roughly 26% to 28%. These findings suggest policymakers should consider nursing home market factors when overseeing and regulating issues of nursing home quality.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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 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

Citations21
Published2011
Admission routes1
Has abstractyes

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