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Record W2729298420 · doi:10.1093/geroni/igx004.305

ACHRU—COMMUNITY PARTNERSHIP PROGRAM FOR OLDER ADULTS WITH DIABETES AND MULTIMORBIDITY

2017· article· en· W2729298420 on OpenAlexaff
Maureen Markle‐Reid, Jenny Ploeg, Kimberly D. Fraser, Kathyrn Fisher, Noori Akhtar‐Danesh, Anne Bartholomew, Amiram Gafni

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsMedicineVitalityGerontologyRandomized controlled trialMental healthQuality of life (healthcare)General partnershipMultimorbidityHealth promotionType 2 diabetesFamily medicineIntervention (counseling)Diabetes mellitusPhysical therapyNursingPublic healthPsychiatryChronic diseaseInternal medicine

Abstract

fetched live from OpenAlex

In response to the complex needs of older adults with Type 2 Diabetes Mellitus and multimorbidity, an interprofessional community-based health promotion program was developed. A pragmatic randomized controlled trial study design was used to conduct the 6-month program comprised of in-home visits, monthly group sessions, and nurse-led coordination of care delivered by Registered Nurses, Dietitians, and fitness leaders from the YMCA or community centre. Compared with the usual care group (n=79), the intervention group (n=80) showed statistically significant and clinically important improvements in the mean SF-12 measured mental health (3.69, p=0.02, 95% CI: 0.60, 6.78), vitality (3.68, p=0.02, 95% CI: 0.57, 6.80) and general health scores (3.56, p=0.02, 95% CI: 0.65, 6.46). These benefits were achieved at no additional cost compared to usual primary care. The results support the effectiveness of the program in improving health related quality of life in older adults with Type 2 Diabetes in community settings.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.060
GPT teacher head0.361
Teacher spread0.301 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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