Two new positive psychosocial measures for persons living with dementia
Bibliographic record
Abstract
INTRODUCTION: Differences in adaptive strategies used by individuals and families living with dementia have the potential to impact day-to-day well-being. The Living Well Inventory for Dementia (LWI-D) is a new measure to capture these strategies and to illuminate new options to support families living with dementia. The Quality of Day Scale (QODS) is a new measure to capture global well-being in persons based on a shorter temporal frame than traditional quality of life measures. This article summarizes the initial evaluation of the LWI-D and the QODS for face validity, content validity, and user acceptability. METHODS: Initial acceptability and feasibility testing were conducted with a sample of 17 community-dwelling individuals with early-stage dementia (Montreal Cognitive Assessment [MoCA] scores of 12-30).After revision and optimization of the two measures, a second pilot test was conducted with a sample of 30 dyads (persons living with dementia and family caregivers) in nursing home, assisted living, and community settings. RESULTS: Data from both pilot studies are reported including item analysis and quantitative and qualitative results. Outcomes related to convergent validity between the LWI-D and the QODS with measures of positive affect-balance, quality of life, and well-being are presented. Within-dyad differences in ratings on both measures are discussed. DISCUSSION: The LWI-D and the QODS are developing measures that warrant further testing and may enhance the ability to (1) identify strengths in living well with dementia, and (2) identify and test new interventions to bolster care and support. Highlights: This article describes the process used to develop and test two new measures for research and clinical practice related to positive psychosocial approaches to dementia.The measures were developed with a team that included persons living with Alzheimer's disease as co-researchers in the process.A novel method of human-centered design was used to cultivate deep empathy, generate options, and conduct small, iterative tests of prototype measures.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".