The <i>Perceived Control Over Pain</i> Construct and Functional Status
Bibliographic record
Abstract
INTRODUCTION: Belief in one's ability to control pain is a significant predictor of health outcomes and is related to improved functional status. The purpose of this study was to introduce a novel formulation of the construct, Perceived Control Over Pain and to test its effects on functional status. METHODS: Participants (N = 301) were primarily African American (92%); and were adults with low income attending a primary care clinic and reporting pain within the past 2 weeks. A cross-sectional design was used with confirmatory factor analysis and structural equation modeling. The Perceived Control Over Pain construct consisted of four measures-two specific measures of control over pain and two general measures of control over life events. Perceived Control Over Pain has not been defined in this way previously. RESULTS: Mean worst pain scores for the past week were 8.4, where "0" (no pain) to "10" (pain as bad as you can imagine). The model demonstrated good construct validity for the components of pain, Perceived Control Over Pain and functional status. Mediation by Perceived Control Over Pain was partial but strong, accounting for a reduction of 29% in the effect of pain on functional status. DISCUSSION: In minority populations with low income, factors such as perceived control over pain and its effect on the outcome of patient function need to be considered. Improving Perceived Control Over Pain has the potential for improving patients' feelings of life control and purpose or meaning in life, and psychological and physical functioning for adults living with 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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".