Self-Management of Pain in Older Persons: Helping People Help Themselves
Bibliographic record
Abstract
OBJECTIVE: This article has the following two primary objectives: 1) to provide a discussion of the self-management of pain for older adults in relation to therapist-assisted cognitive behavioral procedures; and 2) to review the main features of a recently developed manualized pain self-management program for older adults. DESIGN: Literature review. RESULTS: The term self-management has been used loosely in the pain literature to describe a wide variety of programs in which the individual plays an active role in the management of his or her pain. Many of these programs are therapist-/facilitator-assisted and have many commonalities with cognitive behavior therapy. Efficacy evidence for self-management in older adults is mixed although arthritis self-management programs show considerable promise. The incorporation of a therapist/facilitator appears to enhance the effects of self-management programs. CONCLUSIONS: Self-management outcomes may be inconsistent across studies partly because there is very limited standardization and manualization of self-management approaches. A manualized self-management program is described as an example of an approach that could easily be standardized and facilitate future investigations. It would be important for subsequent research to focus on the identification of subgroups of older patients who are most likely to benefit from self-management, and to determine whether self-management improves outcomes of future professionally administered treatments.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".