MétaCan
Menu
Back to cohort
Record W2000579391 · doi:10.1093/ageing/32.1.53

Economic evaluation of a geriatric day hospital: cost-benefit analysis based on functional autonomy changes

2003· article· en· W2000579391 on OpenAlexafffundabout
Michel Tousignant

Bibliographic record

VenueAge and Ageing · 2003
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of Sherbrooke
FundersHealth Canada
KeywordsMedicineAutonomyIntensive care medicineGerontologyGeriatricsNursingPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: to investigate whether the benefits related to a geriatric day hospital programme exceeded the costs, using a cost-benefit analysis based on changes in functional autonomy. DESIGN: a quasi-experimental design with a historical cohort as comparison group. SETTING: the geriatric day hospital programme at the Sherbrooke Geriatric University Institute in the Province of Quebec, Canada. SUBJECTS: 151 geriatric day hospital patients. METHODS: after admission to and at discharge from the geriatric day hospital programme, functional autonomy was assessed by a trained nurse using the Functional Autonomy Measurement System. Based on financial reports, costs associated with resources consumed at the geriatric day hospital programme by each subject were established. The benefit in dollars per day was estimated with a societal perspective through regression equations based on functional autonomy changes related to the geriatric day hospital programme. A model for spreading the benefit per day was proposed: the median time to institutionalisation or death. RESULTS: for every dollar invested in the geriatric day hospital programme, the benefit for the health system was $2.14 (95% confidence interval: $1.72-$2.56). CONCLUSION: based on our sample of Sherbrooke Geriatric University Institute patients, the benefit related to the geriatric day hospital programme seems to exceed the costs.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.580

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)0.0010.000

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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations27
Published2003
Admission routes3
Has abstractyes

Explore more

Same venueAge and AgeingSame topicFrailty in Older AdultsFrench-language works237,207