The Importance of Positive Events When Living With Chronic Pain
Bibliographic record
Abstract
Abstract Chronic pain is a common condition in later life that is related to high levels of anxiety and depression. One reason why chronic pain is related to affective distress is that this condition may prevent people from deriving the same positive emotions from enjoyable activities. Few studies, however, have examined how exposure and reactivity to daily events differ by chronic pain status. We hypothesized that those with chronic pain will have less exposure and less positive affect reactivity to positive daily events compared to those without chronic pain. Participants from the diary substudy of MIDUS (N = 1,733; nChronicPain = 658, nNoPain = 1,075; M = 56 years-old) completed eight interview days. Chronic pain status was unrelated to the frequency of positive events. Multi-level models revealed that although people with chronic pain had lower levels of daily positive affect, they reacted more positively to daily events (γ = -.033, SE = .010, p < .0001). As a result, levels of daily positive affect on days when people experienced a positive event did not vary by pain status (MChronicPain = 2.73, MNoPain = 2.75). People with chronic pain averaged higher levels of daily negative affect compared to people without chronic pain (MChronicPain = .21, M NoPain =.20), but, on days when they experience a positive event, those with chronic pain had a greater decrease in their negative affect. Findings suggest that positive events impact those with chronic pain more than they do individuals without chronic pain.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".