A Content Analysis of Activity Pacing in Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Activity pacing is a common intervention for patients with chronic pain. Over the past decade a number of instruments have been developed to measure this construct, but their comparative psychometric properties have not been examined. OBJECTIVE: To review the psychometric properties of existing measures of activity pacing, and provide suggestions for future research in this emerging area of pain research. METHODS: A narrative review of current measures of activity pacing followed by a discussion of the conceptual and psychometric challenges in this area. RESULTS: Although there is evidence supporting activity pacing as a unitary construct, important differences remain among the various measures in terms of their item content and assumptions. All existing activity pacing measures include items that assess activity regulation, but vary in their specific content. Most importantly, questionnaire items often reflect different purposes of pacing behaviors. DISCUSSION: Current measures of activity pacing are inadequate. New measures are needed that are based on specific theoretical models; these measures should also make the goal or intent of pacing behaviors explicit. Improvements in the assessment of activity pacing will likely lead to a better understanding of the pacing construct and the effects of pacing interventions.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".