Pacing: A Concept Analysis of a Chronic Pain Intervention
Bibliographic record
Abstract
BACKGROUND: The intervention of pacing is regularly recommended for chronic pain patients. However, pacing is poorly defined and appears to be interpreted in varying, potentially contradictory manners within the field of chronic pain. This conceptual lack of clarity has implications for effective service delivery and for researchers' ability to conduct rigorous study. An examination of the background literature demonstrates that while pacing is often one part of a multidisciplinary pain management program, outcome research is hindered by a lack of a clear and shared definition of this currently ill-defined construct. OBJECTIVES: To conduct a formal concept analysis of the term 'pacing'. METHODS: A standardized concept analysis process (including literature scoping to identify all uses of the concept, analysis to determine defining attributes of the concept and identification of model, borderline and contrary cases) was used to determine what the concept of pacing does and does not represent within the current evidence base. RESULTS: A conceptual model including the core attributes of action, time, balance, learning and self-management emerged. From these attributes, an evidence-based definition for pacing was composed and distributed to stakeholders for review. After consideration of stakeholder feedback, the emergent definition of pacing was finalized as follows: "Pacing is an active self-management strategy whereby individuals learn to balance time spent on activity and rest for the purpose of achieving increased function and participation in meaningful activities". CONCLUSION: The findings of the present concept analysis will help to standardize the use and definition of the term pacing across disciplines for the purposes of both pain management and research.
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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 | 0.039 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".