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Record W3092796346 · doi:10.1177/2235042x20963390

Self-management program versus usual care for community-dwelling older adults with multimorbidity: A pragmatic randomized controlled trial in Ontario, Canada

2020· article· en· W3092796346 on OpenAlexafffundabout
Kathryn Fisher, Maureen Markle‐Reid, Jenny Ploeg, Amy Bartholomew, Lauren E. Griffith, Amiram Gafni, Lehana Thabane, Marie‐Lee Yous

Bibliographic record

VenueJournal of Comorbidity · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsImpactMcMaster University
FundersOntario Ministry of Health and Long-Term Care
KeywordsMedicinePsychological interventionRandomized controlled trialQuality of life (healthcare)Mental healthGerontologyAnxietyFamily medicineIntervention (counseling)Health careNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Multimorbidity, the co-existence of 2+ (or 3+) chronic diseases in an individual, is an increasingly common global phenomenon leading to reduced quality of life and functional status, and higher healthcare service use and mortality. There is an urgent need to develop and test new models of care that incorporate the components of multimorbidity interventions recommended by international organizations, including care coordination, interdisciplinary teams, and care plans developed with patients that are tailored to their needs and preferences. PURPOSE: To determine the effectiveness of a 6-month, community-based, multimorbidity intervention compared to usual home care services for community-dwelling older adults (age 65+ years) with multimorbidity (3+ chronic conditions) that were newly referred to and receiving home care services. METHODS: A pragmatic, parallel, two-arm randomized controlled trial evaluated the intervention, which included in-home visits by an interdisciplinary team, personal support worker visits, and monthly case conferences. The study took place in two sites in central Ontario, Canada. Eligible and consenting participants were randomly allocated to the intervention and control group using a 1:1 ratio. The participants, statistician/analyst, and research assistants collecting assessment data were blinded. The primary outcome was the Physical Component Summary (PCS) score of the 12-Item Short-Form health survey (SF-12). Secondary outcomes included the SF-12 Mental Component Summary (MCS) score, Center for Epidemiological Studies of Depression (CESD-10), Generalized Anxiety Disorder (GAD-7), Self-Efficacy for Managing Chronic Disease, and service use and costs. Analysis of covariance (ANCOVA) tested group differences using multiple imputation to address missing data, and non-parametric methods explored service use and cost differences. RESULTS: 59 older adults were randomized into the intervention (n = 30) and control (n = 29) groups. At baseline, groups were similar for the primary outcome and number of chronic conditions (mean of 8.6), but the intervention group had lower mental health status. The intervention was cost neutral and no significant group differences were observed for the primary outcome of PCS from SF-12 (mean difference: -4.94; 95% CI: -12.53 to 2.66; p = 0.20) or secondary outcomes. CONCLUSION: We evaluated a 6-month, self-management intervention for older adults with multimorbidity. While the intervention was cost neutral in comparison to usual care, it was not found to improve the PCS from SF-12 or secondary health outcomes. Recruitment and retention challenges were significant obstacles limiting our ability to assess intervention effectiveness. Yet, the intervention was grounded in internationally-endorsed recommendations and implemented in a practice setting (home care) viewed as a key upstream resource fostering independence in older adults. These features collectively support the identification of ways to recruit/retain older adults and test alternative implementation strategies for interventions that are based on sound principles of multimorbidity management.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.303
Teacher spread0.273 · 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 designRandomized trial
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
Published2020
Admission routes3
Has abstractyes

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