The Influence of Social Structure on Cancer Pain and Quality of Life
Bibliographic record
Abstract
The aim of this study was to investigate whether social structure is associated with cancer pain and quality of life using the Social Structure and Personality Research Framework. This study was a secondary analysis of data from 480 cancer patients. The measurements included socioeconomic variables, self-reported cancer pain using the McGill Pain Questionnaire-Short Form (MPQ-SF), and quality of life measured using the Functional Assessment of Cancer Therapy Scale (FACT-G). The data were analyzed using moderated multiple regression. Cancer pain and quality of life differed significantly with income. The associations between income and pain and quality of life were significant only for the high education group (≥ partial college), and these associations were greater for Caucasians than for their counterparts ( p < .05). When developing interventions, nurses should consider the influence of socioeconomic variables on pain and quality of life while considering possible moderating factors such as education.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".