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Record W7065034961

Developing a process of risk-stratified care coordination for older adults in primary care

2016· dissertation· en· W7065034961 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2016
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Context (archaeology)Data collectionHealth careIdentification (biology)Work (physics)
DOInot available

Abstract

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BACKGROUND \nOlder Canadians with chronic diseases are the highest users of the health care system. Primary health care (PHC) could play a central, coordinating role in assessing older adults and managing their care, but at present lacks specific strategies to fulfil this role. Priorities for enhanced care coordination in PHC include: 1) consistent processes to identify and assess older persons and create individual care plans aligned with risk levels; 2) improved care coordination and system navigation; 3) improved access to appropriate services; and 4) improved patient and caregiver engagement (Heckman et al., 2013; World Health Organization, 2008; Wagner, 2000; Goodwin et al., 2013). This dissertation project aims to understand how a process of risk-stratified care coordination for older adults can be developed and implemented in primary care. Information gathered to answer this question will provide an in-depth understanding of: i) the local context where the process is implemented, including available health and support services; ii) the process of implementing a screening and referral process in primary care, and iii) the experiences of providers, patients and caregivers with implementation to see how the process might be modified and to understand what factors are important for future spread. \nMETHODS \nThe Chronic Care Model (Wagner et al., 1999), a framework to guide care improvements and a multi-level (environmental, organizational, patient, provider, and program) framework for implementation of health innovations (Chaudoir et al., 2013) were used to guide the three study phases. Overalldata collection and analysis followed a mixed methods design, within a developmental evaluation approach. Data were collected using ethnographic observations (phases 1,2,3), informal feedback (phase 2), individual and focus group interviews (phases 1 and 3), and survey (phases 1 and 3) and tracking forms (phase 3). Data were analyzed using appropriate qualitative and quantitative techniques. Patients, family caregivers, and health care providers were purposefully sampled from two Family Health Teams in Ontario (rural and urban). \nRESULTS \nThrough focus group interviews with health care providers, lack of care coordination, information sharing, patient engagement, and service awareness were identified. To address these concerns, a process of risk-screening and care coordination for patients 70 + years of age was developed and implemented through an iterative process, in two primary care clinics. 512 patients were screened for level of risk using the interRAI Assessment Urgency Algorithm (AUA) and care was coordinated for individuals based on level of need. Among those screened, 70% of individuals screened as low risk, 25% were screened as moderate risk, and 5% were screened as high risk. As a result, service referrals were made to self-management, community programs, and specialized geriatric services using an online referral mechanism. Although the screening and referral process is time consuming, health care providers, patients and caregivers identified many benefits including early identification of service need, greater awareness of services available in the community, and improved relationships between patients and providers. \nCONCLUSIONS \nA process of risk-stratified care coordination was developed and implemented in primary care through an ongoing, iterative process with older adults, caregivers, and health care providers. Future research activities should focus on testing these findings in other models of care (e.g. solo-physician practice) and in other regions.

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)0.079
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0130.007
Scholarly communication0.0100.006
Open science0.0040.016
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.217
Teacher spread0.212 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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