Psychological Approaches in the Treatment of Chronic Pain Patients—When Pills, Scalpels, and Needles are Not Enough
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it