Feasibility of Routine Quality of Life Measurement for People Living With Dementia in Long-Term Care
Bibliographic record
Abstract
OBJECTIVES: Maximizing quality of life (QoL) is the ultimate goal of long-term dementia care. However, routine QoL measurement is rare in nursing home (NH) and assisted living (AL) facilities. Routine QoL measurement might lead to improvements in resident QoL. Our objective was to assess the feasibility of using DEMQOL-CH, completed by long-term care staff in video calls with researchers, to assess health-related quality of life (HrQoL) of NH and AL residents with dementia or other cognitive impairment. DESIGN: Cross-sectional study. SETTING AND PARTICIPANTS: We included a convenience sample of 5 NHs and 5 AL facilities in the Canadian province of Alberta. Forty-two care staff who had worked in the facility for ≥3 months completed DEMQOL-CH assessments of 183 residents who had lived in the facility for 3 months or more and were aged ≥65 years. Sixteen residents were assessed independently by 2 care staff to assess inter-rater reliability. METHODS: We assessed HrQoL in people with dementia or other cognitive impairment using DEMQOL-CH, and assessed time to complete, inter-rater reliability, internal consistency reliability, and care staff ratings of feasibility of completing the DEMQOL-CH. RESULTS: Average time to complete DEMQOL-CH was <5 minutes. Staff characteristics were not associated with time to complete or DEMQOL-CH scores. Inter-rater reliability [0.735, 95% confidence interval (CI): 0.712-0.780] and internal consistency reliability (0.834, 95% CI: 0.779-0.864) were high. The DEMQOL-CH score varied across residents (mean = 84.8, standard deviation = 11.20, 95% CI: 83.2-86.4). Care aides and managers rated use of the DEMQOL-CH as highly feasible, acceptable, and valuable. CONCLUSIONS AND IMPLICATIONS: This study provides a proof of concept that DEMQOL-CH can be used to assess HrQoL in NH and AL residents and provides initial indications of feasibility and resources required. DEMQOL-CH may be used to support actions to improve the QoL of residents.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".