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Record W2790405302 · doi:10.1177/2377960817752471

Self-Management of Multiple Chronic Conditions by Community-Dwelling Older Adults: A Concept Analysis

2018· article· en· W2790405302 on OpenAlexafffund
Anna Garnett, Jenny Ploeg, Maureen Markle‐Reid, Patricia H. Strachan

Bibliographic record

VenueSAGE Open Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsSelf-managementGerontologyChronic diseaseDiseaseDisease managementPsychologyPopulation ageingCase managementAssisted livingMedicinePopulationPsychiatryFamily medicineEnvironmental healthComputer sciencePathology

Abstract

fetched live from OpenAlex

The proportion of the aging population living with multiple chronic conditions (MCC) is increasing. Self-management is valuable in helping individuals manage MCC. The purpose of this study was to conduct a concept analysis of self-management in community-dwelling older adults with MCC using Walker and Avant's method. The review included 30 articles published between 2000 and 2017. The following attributes were identified: (a) using financial resources for chronic disease management, (b) acquiring health- and disease-related education, (c) making use of ongoing social supports, (d) responding positively to health changes, (e) ongoing engagement with the health system, and (f) actively participating in sustained disease management. Self-management is a complex process; the presence of these attributes increases the likelihood that an older adult will be successful in managing the symptoms of MCC.

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)0.011
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.345
Teacher spread0.324 · 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 designTheoretical or conceptual
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

Citations63
Published2018
Admission routes2
Has abstractyes

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