Feasibility of Routine Quality-of-Life Assessment in Long-Term Care Homes
Bibliographic record
Abstract
Abstract Maximizing long-term care (LTC) residents' quality of life (QoL) is the primary goal of care. However, most residents have cognitive impairment and care staff time is severely limited, leading to various complexities in measuring QoL. This study developed and assessed the feasibility of an approach to routinely measuring QoL in LTC residents. We used the DEMQOL-CH, a practical, reliable, valid tool, developed in the UK to be completed by care aides to assess QoL in residents with moderate to severe dementia. We recruited 45 care aides in 10 LTC homes in Alberta, Canada who we surveyed on the QoL of 263 residents via video calls. We assessed time to complete; care aide and manager perceived feasibility of completing the DEMQOL-CH; internal consistency and inter-rater reliability of DEMQOL-CH scores; and we conducted cognitive interviews with 7 care aides to assess care aide comprehension of the tool. Time to complete was on average 4 minutes with little variation. Care aides and managers rated using the DEMQOL-CH as highly feasible and valuable. The internal consistency of the DEMQOL-CH score was 0.80. The DEMQOL-CH score inter-rater agreement was 0.73. Cognitive interviews suggested good comprehension overall with some comprehension problems especially in care aides who speak English as a second language. Asking care aides to complete the DEMQOL-CH is highly feasible, requires minor resources, and reliability is high. However, some items caused comprehension and reliability problems. Reasons and possible solutions will be subject to further investigations.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".