Dementia-specific quality of life instruments: a conceptual analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".