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Record W4283211059 · doi:10.1017/s0714980821000726

A Cost-Benefit Analysis of a Group Memory Intervention for Healthy Older Adults with Memory Concerns

2022· article· en· W4283211059 on OpenAlexaffabout
Stevenson Baker, Susan Vandermorris, Nicolaas Paul L.G. Verhoeff, Angela K. Troyer

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsIntervention (counseling)Health careMemory problemsGerontologyPsychologyMedicinePsychiatryDisease

Abstract

fetched live from OpenAlex

This study examines whether memory intervention programs can mitigate health care costs. Research suggests these programs translate to a decreased intention of older adults who are worried about age-normal memory changes to seek traditional outlets for medical/psychiatric help. We employed a cost-benefit analysis approach to analyze the effectiveness of a memory intervention program within Ontario. We leveraged estimates of decreased intentionality to seek physician care following a community-based memory intervention with physician billing profiles to calculate the potential cost savings to the province's health care system. The intervention studied was found to reduce provincial health care spending by $6,094 per program group. This amount exceeds $121.25 in direct costs per attendee associated with administering five program sessions. This analysis justifies further research on how community-based memory and aging programs can offer low-cost solutions to help individuals cope with subjective memory complaints and assist the health care system in prioritizing care for aging patients.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.281
Teacher spread0.264 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissement→Same topicDementia and Cognitive Impairment Research→French-language works237,207→