Pain-related Activity Patterns
Bibliographic record
Abstract
OBJECTIVES: Changes in activity frequently occur as a consequence of ongoing pain. Three activity patterns commonly observed among individuals with ongoing pain are avoidance, overdoing, and pacing. We conducted 2 studies investigating these activity patterns, their interrelationships, and their associations with key psychosocial factors. Study 1 describes the development of a measure, the Patterns of Activity-Pain (POAM-P), to assess these activity patterns; Study 2 examines the psychosocial correlates of these activity patterns. METHODS: In study 1, a sample of 393 individuals with chronic pain responded to a pool of 51 items assessing activity as part of their pretreatment assessment. Item analyses were conducted to create a 30-item measure with 3, 10-item scales assessing avoidance, overdoing, and pacing. In study 2, a sample of 164 individuals attending a follow-up program 3 months after treatment completed the POAM-P along with measures of affect, pain control, and disability. RESULTS: scales demonstrated excellent internal consistency and correlations with other measures provided initial support for construct validity. Avoidance and overdoing were associated with negative psychosocial outcomes whereas pacing was associated with positive outcomes. In contrast to previous studies, pacing and avoidance were unrelated. DISCUSSION: The POAM-P has excellent psychometric properties and may be useful in clinical practice to identify activity patterns associated with poorer functioning and to evaluate interventions intended to modify these activity patterns. The present results support previous findings linking avoidance and various negative outcomes. These results also provide evidence that pacing may be related to positive outcomes after treatment.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".