Psychological Approaches in the Treatment of Chronic Pain Patients—When Pills, Scalpels, and Needles are Not Enough
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a prevalent and costly problem that eludes adequate treatment. Persistent pain affects all domains of people's lives and in the absence of cure, success will greatly depend on adaptation to symptoms and self-management. METHOD: We reviewed the psychological models that have been used to conceptualize chronic pain-psychodynamic, behavioural (respondent and operant), and cognitive-behavioural. Treatments based on these models, including insight, external reinforcement, motivational interviewing, relaxation, meditation, biofeedback, guided imagery, and hypnosis are described. RESULTS: The cognitive-behavioural perspective has the greatest amount of research supports the effectiveness of this approach with chronic pain patients. Importantly, we differentiate the cognitive-behavioural perspective from cognitive and behavioural techniques and suggest that the perspective on the role of patients' beliefs, attitudes, and expectations in the maintenance and exacerbation of symptoms are more important than the specific techniques. The techniques are all geared to fostering self-control and self-management that will encourage a patient to replace their feelings of passivity, dependence, and hopelessness with activity, independence, and resourcefulness. CONCLUSIONS: Psychosocial and behavioural factors play a significant role in the experience, maintenance, and exacerbation of pain. Self-management is an important complement to biomedical approaches. Cognitive-behavioural therapy alone or within the context of an interdisciplinary pain rehabilitation program has the greatest empirical evidence for success. As none of the most commonly prescribed treatment regimens are sufficient to eliminate pain, a more realistic approach will likely combine pharmacological, physical, and psychological components tailored to each patient's needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".