Does the quality of life construct as illustrated in quantitative measurement tools reflect the perspective of people with dementia?
Bibliographic record
Abstract
AIMS: A discussion of the extent to which people with dementia's perspectives on quality of life have been included in quantitative research. BACKGROUND: Capturing the perspective of people with dementia may improve understanding of their quality of life. Quantitative tools to assess quality of life exist, but the extent to which these reflect the perspective of people with dementia has not been evaluated. DESIGN: A discussion paper. DATA SOURCES: Ten tools (designed between 1992-2012) to measure quality of life from the perspective of people with dementia were located from existing reviews. DISCUSSION: Each tool was rated according to the extent to which the developers included the perspectives of people with dementia at three different points of quality of life conceptualization: during quality of life assessment, to identify quality of life domains and to define an overall conceptual framework. This analysis demonstrates that tool developers were inconsistent in their approach to including the perspectives of people with dementia to understand quality of life. The perspective of people with dementia was included primarily to assess, but not to select domains or define overall quality of life. IMPLICATIONS FOR NURSING: Nurses should consider not only who assesses quality of life, but also whose understanding of quality of life is being assessed. CONCLUSION: It is unclear whether the quantitative quality of life literature reflects the perspective of people with dementia. Debate is needed regarding the impact of this issue on the lives of people with dementia.
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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 | 0.302 | 0.548 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".