Learned pacing for adults with chronic pain: A randomised controlled trial feasibility study
Bibliographic record
Abstract
Introduction: Chronic pain can significantly impact on an individual's occupational performance and quality of life. Pacing is a pain management strategy regularly used in occupational therapy practice; however, evidence for its effectiveness has not been established. Objectives: To determine the feasibility of a future randomised controlled trial to investigate the effectiveness of a learned pacing intervention on occupational performance and satisfaction for adults with chronic pain. Methods: A randomised controlled trial feasibility study was conducted with participants randomly assigned to a learned pacing intervention or a waitlist control group. The primary outcome measure was the Canadian Occupational Performance Measure. Results: One hundred and twenty-eight people were screened for eligibility over 36 weeks, with 74 people invited to participate. Twelve were randomly assigned, eight to the learned pacing group and four to the control group. Those receiving the learned pacing intervention had clinically important changes in occupational performance and occupational satisfaction. Participants in the waitlist control group also had clinically important changes in occupational satisfaction. The method design was deemed feasible; however, several improvements would increase the rate of participant recruitment and reduce attrition. Recruitment from multiple sites is required to obtain an adequate sample size of 60. Conclusion: Undertaking a future randomised controlled trial is feasible and warranted to establish the effectiveness of a learned pacing intervention on occupational performance and satisfaction for adults with chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".