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Record W7047590113

The impact of assistive devices on community-dwelling older adults and their informal caregivers

2023· dissertation· en· W7047590113 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Aging in placeActivities of daily livingTracking (education)Life satisfactionHealthy agingLongitudinal studyLife course approachActivity tracker
DOInot available

Abstract

fetched live from OpenAlex

Background \nCanadians are aging and living longer with chronic conditions, multimorbidity, and disabilities, which can have negative impacts on the health and quality of life of both older adults and their informal caregivers. Assistive devices (AD) can be beneficial to community-dwelling older adults and their informal caregivers; however, researchers have not investigated all outcomes of using AD. Two under-investigated outcomes in aging populations are: (a) the change in life satisfaction (LS) over time, and (b) the change in the number of informal caregiving hours received over time. The Consortium for Assistive Technology Outcome Research (CATOR) framework was used to frame the following objectives, which identifies LS and the amount of informal caregiving hours received as key outcomes of AD use. \nObjectives \nThe objectives of this thesis were addressed via three studies: Study 1 (Chapter 3) examined existing evidence on the associations between (a) AD use and LS, and (b) AD use and informal caregiving hours received; Study 2 (Chapter 4) and Study 3 (Chapter 5) investigated the associations between self-reported AD use within the past 12 months (assessed at baseline) and: (a) the change in LS over time (three-years), (b) the change in informal caregiving hours received over time (three-years), respectively. All studies focused on community-dwelling older adults aged 65 years or older. \nMethods \nStudy 1 consisted of a systematic review adhering to the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Studies 2 and 3 used unweighted data from the Tracking and Comprehensive cohorts of the Canadian Longitudinal Study on Aging (CLSA) and multiple linear regression models to investigate the associations between AD use and the change in LS or informal caregiver time. The regression analyses controlled for comprehensive sets of covariates. \nResults \nStudy 1 suggested AD use was not linked with LS and was associated with reductions in informal caregiving hours. However, due to the scarcity of existing studies, their limitations (e.g., high risk of bias, residual confounding, cross-sectional nature), and very low or moderate strength of evidence, conclusions about the associations of interest could not be drawn with certainty. Results from Study 2 did not find evidence of an association between AD use and increases in LS over time, after accounting for covariates (Tracking: n = 5,502, β = 1.16, 95% Confidence Interval [CI] = -0.57 to 2.89; Comprehensive: n = 9,760, β = 0.47, 95% CI = -0.89 to 1.82). Similarly, after controlling for covariates, Study 3 did not find associations between AD use and changes in informal caregiving hours received over time (Tracking: n = 236, β = 3.10, 95% CI = -77.98 to 84.17; Comprehensive: n = 420, β = -5.05, 95% CI = -47.19 to 37.09). \nConclusion \nIn an aging society, empirical evidence regarding the effects of AD on the changes in LS and informal caregiver hours is imperative for evidence-based decision-making and effective recommendations on the provision of AD to older adults. Although the findings of this thesis were non-significant, null findings can be informative because they can contribute to guiding future studies, informing existing theories, and avoiding misleading research conclusions or biased evidence-based practices and policies. \nTo overcome the limitations of existing studies, future research should aim to extend beyond three years, use large sample sizes, conduct analyses based on the type of AD used (e.g., mobility versus vision-related AD) and the duration of AD use (e.g., short, long, intermittent use), and control for additional potential confounders (e.g., device satisfaction, time-varying confounders). LS questions should be specifically tailored to AD use and informal caregivers should be directly interviewed to promote the accuracy of data on informal caregiving hours. Data sets designed to collect information primarily on AD should be used in future investigations to address the research questions in this thesis. These data sets should ideally be culturally representative and have minimal bias (e.g., selection bias, missing data) to assure reliability and generalizability of the findings. \nThis thesis further highlighted various implications for future research, theory, policy and practice. These implications included the complexity of research questions and concepts (i.e., life satisfaction, accurate capture of informal caregiving hours from care receivers and caregivers), overcoming limitations of existing studies, the importance of stratified analysis to inform sub-theories in the CATOR framework, integration of evidence from multiple sources (e.g., experimental studies), funding for improved research, recognition of null findings, and collaborative efforts among stakeholders to make informed decisions related to AD use among community-dwelling older adults.

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)0.003
metaresearch head score (Gemma)0.011
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.225
Teacher spread0.215 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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