The Activity Management Inventory for Pain (AMI-P)
Bibliographic record
Abstract
OBJECTIVES: Activity management is an important treatment component in chronic pain programs. However, there are shortcomings in measures of this construct, leading to inconsistencies in research findings. Here, we describe the development of the Activity Management Inventory for Pain (AMI-P). MATERIALS AND METHODS: The AMI-P was developed by a group of international researchers with extensive expertise in both chronic pain and activity management. The initial evaluation of the AMI-P items included 2 studies that were both conducted in Canadian tertiary pain care centers. RESULTS: The resulting 20-item measure has 3 behavior scales (Rest, Alternating Activity, and Planned Activity), and 4 goal scales (Feel Less Pain, Get More Done, Complete the Task, and Save Energy). The behavior scales evidenced marginal to good internal consistency and test-retest reliability, and a moderate positive association with an existing pacing measure. The Rest and Alternating Activity scales were associated with greater pain interference, the Alternating Activity and Planned Activity scales were associated with less satisfaction with social roles, and the Planned Activity scale was associated with fewer depressive symptoms. The Alternating Activity scale increased significantly from pretreatment to posttreatment. All goal scales were positively associated with all behavior scales. The Feel Less Pain goal scale was positively associated with measures of avoidance and pain interference, while the Get More Done goal scale was negatively associated with measures of depressive symptoms and overdoing. DISCUSSION: The findings support the reliability and validity of the AMI-P scales, while also highlighting the complexity and multidimensional aspects of activity management.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".