Rational-Emotive Behavior Therapy Program for Trauma-Specific Beliefs Among Undergraduate Students: Testing the Effect of A Group Therapy
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: The identification of trauma and posttraumatic stress disorder and its treatment is critically important in contemporary society. This preliminary research aimed to investigate the effect that rational-emotive behavior therapy (REBT) had on trauma-specific beliefs. METHOD: This study used a randomized controlled trial design. The study participants were 182 undergraduate students. A self-report questionnaire which measures trauma-specific irrational beliefs was used for data collection. A trauma-focused REBT manual guided the group intervention. Within x Between-subjects and paired t-test statistic were used for data analysis. RESULTS: The results show that REBT brought about a significant reduction in trauma-specific irrational beliefs among the students in the treatment group compared to their counterparts in the waitlist control group. Finally, the results indicate that the positive gains were significantly maintained by the treatment group at four months follow-up. CONCLUSION: The current study suggests that an REBT program can be helpful in altering trauma-specific irrational beliefs. The authors employed this model of psychological intervention in an African society in which trauma is significant. The authors demonstrated a model for evaluation and a model of intervention that appears to be of a significant and enduring impact as reported in this study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".