Social Determinants and Consequences of Pain: Toward Multilevel, Intersectional, and Life Course Perspectives
Bibliographic record
Abstract
Despite wide endorsement of a biopsychosocial framework for pain, social aspects of pain remain rarely addressed in the context of pain prevention and management. In this review, we aim to 1) examine the broad scope of social determinants and consequences of pain and their interactions across multiple levels of organization, and 2) provide a framework synthesizing existing concepts and potential areas for future work on social aspects of pain, drawing upon socioecological, intersectional, and life course approaches. Integrating interdisciplinary theory and evidence, we outline pathways through which multilevel social factors and pain may affect each other over time. We also provide a brief summary of intrapersonal aspects of pain, which are thought to operate at the interface between individuals and the social context. Progressing from micro- to macrolevel factors, we illustrate how social determinants of pain can directly or indirectly contribute to pain experiences, expression, risk, prognosis, and impact across populations. We consider 1) at the interpersonal level, the roles of social comparison, social relatedness, social support, social exclusion, empathy, and interpersonal conflict; 2) at the group or community level, the roles of intimacy groups, task groups, social categories, and loose associations; and 3) at the societal level, the roles of political, economic, and cultural systems, as well as their policies and practices. We present examples of multilevel consequences of pain across these levels and discuss opportunities to reduce the burden and inequities of pain by expanding multilevel social approaches in pain research and practice. PERSPECTIVE: Despite wide endorsement of a biopsychosocial framework for pain, social aspects of pain are often unclearly defined, hindering their use in pain prevention, management, and research. We summarize the scope of social aspects of pain and provide a framework synthesizing existing concepts and potential areas for future work.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".