The Comparison of the Effect of Transactional Model-Based Teaching and Ordinary Education Curriculum- based Teaching Programs on Stress Management among Teachers
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Regarding the effect of teachers' stress on teaching and learning processes, the researchers decided to provide a stress management program based on Transactional Model to solve this teachers' problems. Thus, this study is going to investigate the effect of Transactional Model- based Teaching and the Ordinary Education Curriculum- based Teaching programs on Yazd teachers. METHODS: The study was a semi- experimental one. The sample population (200 people) was selected using categorized method. The data were collected via PSS Questionnaire and a questionnaire which its validity and reliability had been proved. Eight teaching sessions were hold for 60-90 min. Evaluation was performed in three steps. The data were described and analyzed using SPSS software version 15. Value of P<0.05 was considered as significant. RESULTS: The participants were 200 people of Yazd teachers of primary schools. Mean age of group 1 and 2 was 42.05±5.69 and 41.25±5.89 respectively. Independent T- Test indicated a significant mean score (p=0.000) due to perceived stress of interference groups in post interference step and follow-up one respectively. CONCLUSION: Results showed a decreasing effect of both programs, but the Transactional Model- based interference indicated to decrease stress more than the other.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".