The effect of reality therapy on deprestion symptoms and chronic pain in women with a veteran spouse and the mental health of girls aged 18 to 20 years
Bibliographic record
Abstract
The aim of this study was to evaluate the the effect of reality therapy on depression symptoms and chronic pain in women with a veteran spouse and the mental health of girls aged 18 to 20 years. The quasi-experimental study was a pretest-posttest with a control group. The statistical population of the study includes all women with veteran spouses and daughters aged 18 to 20 years in Gorgan in 2019. Among the spouses and children of veterans referring to the Gorgan Shahed Counseling Center, 60 people were selected by available sampling method and randomly selected in experimental group 1 (15 mothers), experiment 2 (15 children), control 1 (15 mothers), control 2 (15 children) were placed. The experimental groups received reality therapy training (8 sessions of 90 minutes) and the control groups did not receive any intervention until the end of the study. The research instruments were Depression Inventory (Beck, 1988), Chronic Pain Questionnaire (McGill, 1997) and General Health Questionnaire (Goldberg & Hiller, 1979). Data were analyzed using univariate analysis of covariance in spss.22 software. The results showed that reality therapy has a significant effect on reducing depression, chronic pain in women and increasing the general health of children (p<0.001). Reality therapy can be used as an effective intervention method to reduce depression and chronic pain in women with a veteran spouse and increase the mental health of their children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".