Self-perceived burden in chronic pain: Relevance, prevalence, and predictors
Bibliographic record
Abstract
Chronic pain is a debilitating condition that can have an impact on various facets of interpersonal functioning. Although some studies have examined the extent to which family members are affected by an individual's chronic pain, none have examined patients' perceptions of feeling that they have become a burden to others. Research on self-perceived burden in different medical populations, such as cancer, amyotrophic lateral sclerosis, and stroke, has shown that it is associated with physical symptoms and, more robustly, with psychological difficulties and concerns. The present study examined the prevalence and predictors of self-perceived burden in a tertiary chronic pain sample. Participants were consecutive patients (N = 238) admitted to an outpatient, interdisciplinary, chronic pain management program at a rehabilitation hospital. At admission, participants completed a battery of psychometric questionnaires assessing self-perceived burden, as well as a number of clinically relevant constructs. Their significant others (n = 80) also completed a measure of caregiver burden. Self-perceived burden was a commonly reported experience among chronic pain patients, with more than 70% of participants endorsing clinically elevated levels. It was significantly correlated with pain intensity ratings, functional limitations, depressive symptoms, attachment anxiety, pain self-efficacy, and caregiver burden. Self-perceived burden was also correlated with an item assessing suicidal ideation. In a hierarchical regression model, depressive symptoms, pain self-efficacy, and adult attachment significantly predicted self-perceived burden after controlling for demographic and pain-related variables. In conclusion, self-perceived burden is a clinically relevant and commonly reported interpersonal experience in patients with longstanding 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".