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Record W2529372398

Personal Characteristics and Risk Factors Associated with Economic Trade-offs and Financial Management Difficulties in Older Adult Home Care Populations

2013· dissertation· en· W2529372398 on OpenAlexfundaboutno aff
Lee Anne Davies

Bibliographic record

VenueUWSpace (University of Waterloo) · 2013
Typedissertation
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsBusinessFinancial riskRisk managementFinanceMedicine
DOInot available

Abstract

fetched live from OpenAlex

People are living longer and this increases the risk of encountering financial difficulties when trying to make fixed retirement incomes stretch over additional years. Increased life expectancies also increase the likelihood of encountering a health issue including cognitive or functional declines that can affect money management capabilities. There are government entitlement programs available to assist retired Canadians but these programs are under review and new policies are being considered in order to reduce fiscal pressures. At the same time, family roles and structures are changing and informal supports available to previous generations may be reduced. As well, if an older person’s money is poorly managed there will be fewer options for maintaining quality of life in the retirement years. This increases the risk of poverty for older Canadians. 
\nThe goals of this research are to: understand individual risk factors including demographic, clinical and social support characteristics among Canadians age 55 and over who are experiencing poverty; to understand the predictive characteristics for moving into or exiting from poverty; and, to develop a comprehensive description of those who have great difficulty managing their finances. In order to achieve this, data from the interRAI Home Care (RAI-HC) assessment instrument were used. Three regions, Winnipeg Regional Health Authority (WRHA), Nova Scotia and Ontario, were analyzed in order to understand the characteristics of those making economic trade-offs (N=345,678). Data from the province of Ontario was used to understand predictors of poverty transitions (N=47,653) and to develop a profile of those having great difficulty managing their finances (N=321,816). In order to answer each question of interest multivariable logistic regression modeling was used. 
\nResults from the analyses found that those most at risk for making economic trade-offs were in the age 55 to 64 group, had three or more depressive symptoms and were separated or divorced. Gender was not a risk factor. Regional differences for poverty risks were also identified showing greater risks for those experiencing mental health issues in WHRA, for those with more clinical indicators in Ontario, and for younger residents (age 55 to 64) in Nova Scotia. The longitudinal analyses on poverty transitions revealed that females who had completed at least a grade eight education were more likely to exit poverty. The younger group (age 55 to 64 years) with three or more depressive symptoms and experiencing unstable health were more likely to enter poverty. Marriage and older age were protective from the risks of entering poverty. Results from the analyses of those likely to have great difficulty with financial management indicated that deficits in cognition, procedural memory and function increased the risk of being unable to manage personal finances. Gender and marital status were not associated with financial management difficulty.
\nThe development of a profile of those who are making economic trade-offs and those at risk of having difficulty with financial management provides the opportunity for early intervention. Those who have not reached the traditional retirement age of 65 have an increased risk of poverty. Understanding characteristics of those who exit poverty will help establish policies and programs that will assist older Canadians. These are important issues due to the increased number of post-employment years that Canadians are living and the national focus on fiscal restraints. The management of finances has received minimal scientific research and evidence is needed to understand when changes in capability occur and how these changes may be supported by appropriate levels of assistance and supportive devices.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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