The Impact of online training of Positive Thinking Skills on Social Adjustment and Alexithymia in Transsexual Students
Bibliographic record
Abstract
Introduction: Alexithymia and social adjustment are among the problems that transsexual students encounter. Teaching positive thinking skills has been considered as a therapeutic technique for social adjustment and behavioral issues in children and adolescents. Hence, this study was conducted aiming to determine if teaching positive thinking skills would contribute to improved social adjustment and alexithymia in transsexual students.Methods: The present study was semi-experimental in terms of research methods, and an applied one in terms of the research objective. It was performed as a pre-test, post-test study composed of two experimental and control groups for three months (From May to July 2021). The research population was made up of all transsexual secondary high school students referring to schools and education departments' counseling offices in Tehran. Out of the research overall population, a total of 30 students selected through convenient sampling composed the research sample. They were randomly divided into two experimental and control groups. The former received teaching in positivity as an intervention through Google Meet, whereas the latter received no intervention. The data collection tools for this study were Sinha & Singh's Adjustment Inventory for School Students (AISS) (1993), The Toronto Alexithymia Scale (TAS-20) (1994), and the positive thinking skills framework by Seligman, Steen, Park, and Peterson (2005). Statistical analysis was performed using SPSS software version 23. Results: The results displayed that teaching positive thinking skill was effective in social adjustment in transsexual students (P=0.002). Teaching positive thinking skills was also found to have a significant effect on alexithymia in students (P=0.007). Conclusion: Teaching positive thinking skills was shown to make a great contribution to transsexual students' social adjustment and alexithymia.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".