Chronic disease management models in nursing homes: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Nursing home (NH) residents experience a high burden of chronic disease. Chronic disease management (CDM) can be a challenge, as the context of care provision and the way care is provided impact care delivery. This scoping review aimed to identify types of chronic diseases studied in intervention studies in NHs, influential contextual factors addressed by interventions and future CDM research considerations. DESIGN: The scoping review followed guidelines by Arksey and O'Malley (2005) and Levac, Colquhoun and O'Brien (2010). Six reviewers screened citations for inclusion. Data extraction was performed by one reviewer and verified by a second reviewer. DATA SOURCES: We searched four databases: CINAHL, EMBASE, PubMed and Scopus, in March 2018. ELIGIBILITY CRITERIA: Studies were included if (1) aim of intervention was to improve CDM, (2) intervention incorporated the chronic care model (CCM), (3) included NH residents, (4) analysed the efficacy of the intervention and (5) sample included adults over age 65 years. Studies were limited to English or French language and to those published after 1996, when the CCM was first conceptualised. DATA EXTRACTION AND SYNTHESIS: Extracted information included the type of chronic disease, the type and number of CCM model components used in the intervention, the method of delivery of the intervention, and outcomes. RESULTS: On completion of the review of 11 917 citations, 13 studies were included. Most interventions targeted residents living with dementia. There was significant heterogeneity noted among designs, outcomes, and type and complexity of intervention components. There was little evaluation of the sustainability of interventions, including feasibility. CONCLUSIONS: Research was heavily focused on management of dementia. The most commonly included CCM components were multidisciplinary care, evidence-based care, coordinated care and clinical information systems. Future research should include subjective and objective outcomes, which are meaningful for NH residents, for common chronic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".