The role of everyday emotion regulation on pain in hospitalized elderly: Insights from a prospective within-day assessment
Bibliographic record
Abstract
Pain management is still an unresolved issue among the general elderly patient population in institutions. It is proposed that everyday emotion regulation (i.e. self-supporting maintenance or change in positive and negative emotions) performed by hospitalized elderly can help reduce pain intensity. This argument is based on (1) robust evidence in life span research of elderly's high ability for emotion regulation in the midst of everyday life and (2) experimental evidence from pain research that simple strategies to regulate emotions impact pain intensity. A prospective within-day study was designed to (1) empirically trace the occurrence of emotion regulation over specific sampling episodes, (2) assess the impact of this regulation on end-of-episode pain intensity, and (3) consider the effects of socio-demographic, psychological, and clinical factors on emotion regulation and its relationship to pain intensity. Thirty patients (mean age 78.8) of a geriatric facility provided ratings of emotional states and pain intensity. Emotion regulation was defined as maintenance/recovery of desirable emotional states and computed for individual emotions (positive feelings, anger, anxiety, and mild depressed feelings) and globally to reflect the number of emotions successfully regulated. Multilevel analyses found emotion regulation to be prospectively related to pain intensity, for both global and anxiety regulation. While this relationship held across the sample, lower emotion regulation was found for old-old (vs. young-old), males (vs. females), and patients living alone (vs. with others). Results suggest the possibility that promoting emotion regulation as self-management strategy could contribute to cost-effective pain management in general or targeted elderly populations.
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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 | 0.005 | 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 | 0.000 |
| 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".