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Record W4390956991 · doi:10.5334/ijic.icic23237

Assessing the Scalability of a Community-Based Self-Management Intervention for Older Adults with Diabetes and other Chronic Conditions – The Aging, Community and Health Research Unit Community Partnership Program (ACHRU-CPP)

2023· article· en· W4390956991 on OpenAlexaffabout
Melissa Northwood, Maureen Markle‐Reid, Rebecca Ganann, Kathryn Fisher

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineNursingGeneral partnershipAging in placeIntervention (counseling)Integrated careCommunity healthGerontologyHealth carePublic health

Abstract

fetched live from OpenAlex

Background: A multi-pronged approach was undertaken to assess the scalability of the Aging, Community and Health Research Unit – Community Partnership Program (ACHRU-CPP), a complex, integrated 6-month self-management program for community-living older adults with diabetes and multimorbidity and their caregivers, in two Canadian provinces. The ACHRU-CPP was co-designed by patients, caregivers, home and community-care providers, and researchers, in response to a gap identified by older adults in the self-management of their diabetes and other conditions. The intervention was tested in a feasibility study and an earlier pragmatic trial found that older adults who received the ACHRU-CPP experienced greater improvements in quality of life and self-management, and greater reduction in depressive symptoms, compared to those who received usual diabetes care, at no additional cost to society. Delivered by a collaborative team of primary care providers (registered nurses, registered dietitians) and a community program coordinator, key components include home visits, group wellness sessions, team-based case conferences, caregiver support, interprofessional collaboration, and nurse-led care coordination. Scalability assessment is an integral phase of the current research program, which evaluated the effectiveness and implementation of the ACHRU-CPP in four settings in Ontario (ON) and Prince Edward Island (PEI). Methods: Multiple methods were used to assess scalability of the ACHRU-CPP in ON and PEI: an environmental scan, individual key informant interviews, and qualitative and quantitative data from both the foundational studies and the current trial of ACHRU-CPP implementation and effectiveness. The Intervention Scalability Assessment Tool (ISAT) guided data collection and analysis. The environmental scan was conducted with strategic input from Scalability Working Groups in ON and PEI. These groups were comprised of members of the program’s governance structure, including patient and public research partners, researchers, primary care and community service providers and administrators, and policy- and decision-makers. These partners advised on relevant research and policy documents, identified potential key informants (i.e., policy- and decision-makers at the local, provincial, and national levels), and will participate in scalability assessment workshops in ON and PEI in late 2022, to finalize the scalability assessment, and identify components of the intervention to be strengthened, and barriers to be addressed to enhance the scalability of the program, in each province. Results: To date, the results of the scalability assessment have identified areas of strength and limitations within the program, most notably issues with health human resources, mixed results regarding the effectiveness of the program, and other resource gaps. Conclusion: This study has evaluated the scalability of a community-based program for older adults with diabetes and multiple chronic conditions that integrates primary and community care. It also provides valuable insight on the usefulness and feasibility of the ISAT for assessing scalability, as well as strategies to engage strategic practice, policy, and public partners in the process. Next steps: While scale-up of the ACHRU-CPP may be merited due to the prevalence of diabetes and multimorbidity among older adults, and alignment of this program with health policy, several barriers need to be addressed before scale-up can be recommended.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.124
GPT teacher head0.466
Teacher spread0.343 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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