Patterns of pain-related activity: replicability, treatment-related changes, and relationship to functioning
Bibliographic record
Abstract
Changes in activity patterns frequently accompany the experience of chronic pain. Two activity patterns, avoidance and overdoing, are hypothesized to contribute to the development of ongoing pain and pain-related disability, while activity pacing is frequently introduced to enhance pain management and functioning. Two studies were conducted to assess whether reliable subgroups with differing activity patterns could be identified in different pain populations and to evaluate changes in these subgroups after a group format, pain management program. In study 1, individuals with ongoing pain being assessed for treatment at 2 different tertiary care pain centres completed a measure of pain-related activity. Separate cluster analyses of these samples produced highly similar cluster solutions. For each sample, a 2-cluster solution was obtained with clusters corresponding to the activity patterns described by the avoidance-endurance model of pain. In study 2, a subset of individuals completing a 12-session, group format, pain management program completed measures of pain-related activity, pain intensity, and physical and psychological functioning at the beginning and end of the program. At the conclusion of the program, 4 clusters of pain-related activity were identified. Individuals who used high levels of activity pacing and low levels of avoidance consistently reported significantly better functioning relative to all other individuals. Observed changes in activity patterns from pre-treatment to post-treatment suggested that decreasing the association between activity pacing and avoidance was associated with better functioning. These results have implications for both the assessment of activity pacing and for its use as an intervention in the management of ongoing 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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".