The Effect of Life Review Group Therapy on Elderly With Chronic Pain
Bibliographic record
Abstract
Objectives: Chronic pain is a common problem in the elderly. The prevalence of pain indicates that among the Iranian older population who are living in nursing homes, at any specific time, at least 72.8% experience pain. Research designed as a structured review of one’s life is helpful, even is therapeutic in elderly. The aim of life review therapy increases life satisfaction, improves self-esteem, and helps elderly to cope with crises, losses, life transitions and providing acceptance of their life`s realities in coping and resolving their own past conflicts. Methods & Materials: Via this descriptive, case-control research, forty residents with persistent pain of 4 nursing homes in Tehran were selected. All subjects were asked to give their own demographic details and pain status following the McGill Pain Questionnaire. Randomly they got allocated into two groups. First group, conducting the complete life review therapy guide and the second one as control group. During the therapy time, patients reconstruct their life story and examine both positive and negative experiences, with the therapist as a coach. Effects of life review therapy on such elderly were examined via pain questionnaire after therapy periods and were compared with basic levels. Results: The results showed a significant difference between two groups.Reduction in pain questionnaire scores in first group compare with control group were significant. Conclusion: The implications of these results are discussed with respect to the utility of applying life review therapy for elderly with chronic pain
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".