Bibliographic record
Abstract
The purpose of this study was to compare schema therapy and neuro-linguistic planning on reducing anxiety in the patients with pervasive anxiety disorder.The semi-pilot research pattern was a type of pretest-posttest and control group.The statistical population of the include all of the patients with pervasive anxiety disorder that visited in the imam hospital of divandareh city in the first half of 1394.For this purpose 30 patients (male and fmale) with pervasive anxiety disorder were selected based on DSM-5 criterions and 7 questions of pervasive anxiety disorder (GAD-7) scale and they were randomly divided into three groups، include schema therapy group، neuro-linguistic planning group and control group.Schema therapy group were trained 10 session (90minutes) neuro-linguistic group were trained 8 session (90minutes) according to the protocol.Data analized by using multivariate analysis of covariance (MANCOVA).Results showed that there are significant differences between anxiety scores of schema therapy group and neuro-linguistic planning group with control group.These means that the post test of two experimental groups were reduced than the control group،but there were no significant difference between two experimental groups.Conclusion: it can be concluded from the result of these study that interventions based on schema therapy and neuro-linguistic planning is phanning is effective in the reducing of anxiety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.889 | 0.766 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".