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Record W4390957005 · doi:10.5334/ijic.icic23398

Co-design of an integrated home-based primary care model for older adults living with frailty in Ontario, Canada

2023· article· en· W4390957005 on OpenAlexaffabout
Sophiya Garasia, Harleen Badesha, Aruna Mitra, Joyce Oiwun Cheung

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsBrampton Civic Hospital
Fundersnot available
KeywordsFocus groupGeneral partnershipNursingIntegrated careHealth carePopulationService (business)Theory of changeAging in placeMedicineGerontologyBusinessSociologyPolitical scienceMarketingEnvironmental health

Abstract

fetched live from OpenAlex

Background: The demand for home care is predicted to increase with increasing senior population and the aging-at-home phenomenon. Countries world-wide are preparing for this growing demand by making changes to their health care and social care systems. This change is much needed given that, globally, evidence shows that seniors are experiencing fragmented care particularly when they are living with frailty. To address this problem, the Central West Ontario Health Team in Canada is creating an integrated home-based primary care model for individuals 65+ who are living with frailty. At the ICIC23 Conference, I plan to share our research findings, design of the program, learnings from every phase of our co-design and change management approach, and findings from the evaluation of the pilot program. Methods: The program is being co-designed (using Experienced Based Co-Design) in collaboration with patients, caregivers, primary care, hospitals, the municipality, mental health organizations, social workers, home and community care, and other community groups. To date, the co-design team has worked together in partnership to share experiences, segment the population, conduct an environmental scan of documents, and conduct quantitative and qualitative research. First, the team performed descriptive data analysis of hospitalization and community data, and then, three separate focus groups were held with local organizations, service providers, and patients and caregivers (n=60). The data that emerged from the focus groups was then thematically analyzed. The co-design team is now finalizing the program as it is set to be piloted in January 2023, at which point, the program will be evaluated (on measures such as emergency department (ED) diversion and patient experience), updated, and scaled up appropriately. Results: Research showed that 80% of Alternate-Level-of-Care days in the Region are among those who are 65+. A cohort of the older adult population are also high users of the ED who are being discharged home each time. Reported reasons for high ED use include isolation, confusion about the health care system, caregiver burnout, and not receiving proper care in the community in a timely manner. Our research also identified frail older adults to be particularly vulnerable. The program I will describe in the presentation will aim to address these issues. It is novel to Ontario, Canada, and will consist of a screening process to identify “at risk” older adults presenting to the ED, a comprehensive assessment, and ongoing access to an intraprofessional team (consisting of physicians, nurses, physiotherapists, nutritionists, pharmacists, and mental health professionals) that will work together on a shared care plan for each patient. All the tools are being adapted to meet a multi-faceted definition of frailty as well as local needs given that the Central West Region is one of the most ethnically diverse regions in the province. By co-designing, the aim is to implement an integrated program that matches the preferences of patients and their family-members and caregivers. Discussion: The learnings will be helpful to the many groups world-wide who are also planning on creating integrated co-designed primary-care based programs for diverse older adults experiencing frailty.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.029
GPT teacher head0.329
Teacher spread0.299 · 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 designQualitative
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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