Whose Quality of Life is it Anyway? Why Not Ask Seniors to Tell US about IT?
Bibliographic record
Abstract
Three hundred and thirty-one older adults participated in a study designed to examine their perceptions of what constitutes a reasonable quality of life. Participants responded to an open-ended questionnaire in which they were asked to state their priorities, preferences, aspirations, and concerns about their present and future quality of life. Responses were subjected to a principal components factor analysis which yielded four factors: 1) respondents' demands for specific guarantees; 2) respondents' aspirations and expectations for future quality of life; 3) fears and anxieties; and 4) external factors presenting a threat to quality of life. These factors accounted for 15 percent, 12 percent, 9.2 percent, and 7.1 percent, respectively, of the total variance. Additionally, data obtained from in-depth interviews with thirty-seven older adults were analyzed using a qualitative approach. Contrary to stereotypic notions that elderly persons are frail, vulnerable, and resigned to deteriorating conditions of well-being in late life, the results of both the qualitative and quantitative components of the study showed the majority of respondents as having clear demands for autonomy, control, and independence in making decisions, including the decision to terminate life. Implications are discussed in terms of future research on quality of life of older adults.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".