Psychotherapy and chronic pain management: a quantitative study evaluating the contribution of psychotherapy to quality of life and treatment compliance in chronic disease patients
Bibliographic record
Abstract
BACKGROUND: The psychology of pain is an important field of study that focuses on understanding the psychological factors associated with pain and developing effective approaches to its management. Pain is a complex sensation that affects a person's physical and mental well-being, and psychological factors can have a significant impact on the perception, response and coping with pain. This research study examines the contribution of psychotherapy in managing chronic pain and improving quality of life and treatment adherence. PARTICIPANTS AND PROCEDURE: The sample consisted of 87 participants who completed the McGill Pain Assessment Questionnaire, SF-36 Quality of Life questionnaire, and the Morisky Medication Adherence Scale (MMAS-8) questionnaire. Two groups were created: one group received psychotherapy to manage pain and illness, while the other group either did not receive psychotherapy or had no contact with this therapeutic method. RESULTS: The results showed that patients who received psychotherapy had higher scores in the dimensions of mental health, vitality, general health, physical pain, physical functioning, and social functioning compared to patients who did not receive psychotherapy. Statistical analysis confirmed significant differences between the two groups. Additionally, psychotherapy was associated with higher treatment adherence, as indicated by the mean scores of patients receiving psychotherapy compared to those who did not. CONCLUSIONS: This suggests that psychotherapy can contribute to increased treatment adherence. The results clearly show that patients who received psychotherapy have significantly higher levels of mental health, vitality, general health and functioning compared to patients who did not receive psychotherapy.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".