Measuring the Experience and Perception of Suffering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: assess psychometric properties of scales developed to assess experience and perception of physical, psychological, and existential suffering in older individuals. DESIGN AND METHODS: scales were administered to 3 populations of older persons and/or their family caregivers: individuals with Alzheimer's disease (AD) and their family caregivers (N = 105 dyads), married couples in whom 1 partner had osteoarthritis (N = 53 dyads), and African American and Hispanic caregivers of care recipients with AD (N = 121). Care recipients rated their own suffering, whereas caregivers provided ratings of perceived suffering of their respective care recipients. In addition, quality of life, health, and functional status data were collected from all respondents via structured in-person interviews. RESULTS: three scales showed high levels of internal consistency, test-retest reliability, and convergent and discriminant validity. The scales were able to discriminate differences in suffering as a function of type of disease, demonstrated high intra-person correlations and moderately high inter-person correlations and exhibited predicted patterns of association between each type of suffering and indicators of quality of life, health status, and caregiver outcomes of depression and burden. IMPLICATIONS: suffering is an important but understudied domain. This article provides valuable tools for assessing the experience and perception of suffering in humans.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it