A Longitudinal Analysis of Discrete Negative Emotions and Health-Services Use in Elderly Individuals
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that everyday, discrete negative emotions-anger, frustration, sadness, and fear-relate to health-service use in later life. METHOD: Community-dwelling adults (n = 345) ages 72 to 99 were interviewed about the frequency of recently experienced emotions. Physician visits and hospital admissions in the subsequent 2 years were outcomes. Covariates included prior use of health services, chronic illness, functional status, and demographics. RESULTS: Age, education, and gender moderated relations between negative emotions and health care use. More frustration was associated with fewer physician visits among older individuals. Sadness was associated with more hospital admissions for women. Among those with more education, frequent anger was associated with more physician visits. Projected effects of negative emotions resulted in increases in health-service use, ranging from 18% to 33%. DISCUSSION: The interactions indicate the importance of negative emotions and the larger social and developmental context in health care services use among elderly individuals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".