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Record W2322633573 · doi:10.1017/s1041610216000417

Dementia-specific quality of life instruments: a conceptual analysis

2016· review· en· W2322633573 on OpenAlexaff
Pierre Missotten, Gilles Dupuis, Stéphane Adam

Bibliographic record

VenueInternational Psychogeriatrics · 2016
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsycINFODementiaAdaptation (eye)Quality of life (healthcare)Construct (python library)MEDLINETheoretical definitionPsychologyConceptual frameworkConceptual modelCINAHLGerontologyQuality (philosophy)Computer scienceMedicinePsychological interventionSociologyPsychotherapistPsychiatryDiseaseSocial scienceEpistemologyDatabase

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past 20 years, many researchers have worked in developing various methods for measuring quality of life (QoL) of people with dementia. The aim of this review is to develop the conceptual frameworks of the dementia-specific QoL instruments, to identify their evolution over time and to provide elements of reflection on the QoL concept in dementia and its evaluation. METHODS: An electronic search was conducted on PsycINFO and MEDLINE databases, from January 1985 to June 2015 using a combination of key words that include QoL, dementia, and review. RESULTS: The analysis of the conceptual frameworks of the 18 selected dementia-specific QoL tools shows a great diversity in: (1) the QoL definitions (e.g. health-related QoL definitions, QoL definitions based on Lawton's work, or similar to this latter); (2) the theoretical QoL models (e.g. Lawton' work and modified Lawton, adaptation, personhood); (3) the domains and dimensions; (4) the way to construct the instrument (e.g. development based on literature, opinion of the experts), and (5) the items' formulation (e.g. use of criterion of intensity or frequency). CONCLUSIONS: There are different conceptual frameworks in the dementia-specific QoL measures with improvements over time (e.g. inclusion of interesting concepts such as adaptation, taking into account the views of patients themselves). Each of the conceptual parameters (definitions, models, domains, and dimensions) is discussed to identify the scales that are conceptually the strongest. Through their review, recommendations for future instrument refinement and development are discussed and a new QoL definition is proposed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.091
GPT teacher head0.439
Teacher spread0.348 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations49
Published2016
Admission routes1
Has abstractyes

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