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Record W7067421438

Measuring Quality of Life in Persons with Dementia

2023· dissertation· en· W7067421438 on OpenAlexfundaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersFondation Brain CanadaAlzheimer's Association
KeywordsDementiaQuality of life (healthcare)Reliability (semiconductor)Activities of daily livingActive listeningHealth related quality of lifePsychometricsMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Dementia is a debilitating health condition affecting all aspects of an individual’s well-being. Quality of life (QOL) and health related quality of life (HRQOL) assessments play a large role in understanding the limitations within the patients’ lifestyle and helping care providers manage their needs better. Therefore, our aims for this thesis were twofold: 1. Explore the symptoms, behaviors, or activities deemed as priorities by caregivers for monitoring dementia progression, impact of treatment, or exploring alternative care, and 2. Examine the measurement properties of QOL and HRQOL measures used for people with dementia living in the community. Methods: For the first aim, a qualitative descriptive study was performed with caregivers of individuals living with dementia. Caregivers from various regions in Canada were enlisted, and virtual listening sessions took place between November 2022 and January 2023. Open-ended questions were employed to prompt participants to express their viewpoints. For the second aim, a systematic review guided by COsensus-based Standards for the selection of health Measurement Instruments (COSMIN) was performed to identify literature surrounding measurement properties of QOL and HRQOL measures used in community-dwelling adults with a diagnosis of dementia. A search was performed through four databases (Ovid MEDLINE, EMBASE, CINAHL, and PsychInfo) to identify literature published up until June 2022, followed by abstract and title screening and full-text review, which was performed in duplicate. Measurement properties extracted included structural validity, internal consistency, reliability (test-retest and inter-rater reliability), construct validity, and responsiveness. Risk of bias assessments and quality assessments were also performed for all identified QOL and HRQOL measures. Results: Through the listening sessions, we identified that mobility limitations, social interactions, emotions, feeding behaviors, cognitive difficulties and extrinsic factors were priority topics that caregivers wanted to highlight when assessing QOL for people with dementia. There were 13 dementia-specific and generic QOL and HRQOL measures included in the systematic review. Results showed varying quality of evidence for each of the measures with many having a moderate to very low score for some of the measurement properties. Conclusion: The first study showed dementia caregivers’ perspectives on how routine activities, mobility, social interactions, and behaviors, are important pillars of QOL and should be assessed in clinical and research settings for individuals living with dementia. The second study highlighted the importance of rigorously testing QOL instruments in order to provide accurate measurements when evaluating health concerns and impact of therapy. QOL measures can help researchers and healthcare providers obtain a comprehensive assessment of the individual they are treating.

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)0.012
metaresearch head score (Gemma)0.037
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 score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.282
Teacher spread0.234 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

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