The Effectiveness of Positive Psychotherapy on Pain Perception and Death Anxiety in the Elderly
Bibliographic record
Abstract
One of the undeniable realities of aging is recognizing the reality of death and the resulting anxiety, as well as the existence of pain, which is a common experience and a serious problem in old age and can weaken compatibility among the elderly. Therefore, the aim of this study was to investigate the effectiveness of positive psychotherapy on pain perception and death anxiety in the elderly. It was a semi-experimental study with pretest-posttest design including control group. The statistical population of the present study included all elderly people in Aligudarz, Iran in 2018. The statistical sample consisted of 30 people who were randomly selected based on the entrance criteria of the study and divided to two experimental and control groups (15 people in each group). McGill Pain Questionnaire developed by Melzak, and the Templer Death Anxiety Scale were used to collect the data. The experimental group received 8 positive psychotherapy sessions each lasting for 90 minutes, while the control group received no intervention. Data were analyzed using univariate analysis of covariance (ANCOVA) in SPSS software version 25. The results of the study showed significant reduction of pain perception and death anxiety (P<0.01) experiment group compared to control group. Therefore, positive psychotherapy was effective on death anxiety and pain perception in the elderly. Based on the results of this study, it is recommended that positive psychotherapy be used to reduce pain perception and death anxiety among elderly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".