The Effect of SI-G Training on Increase SQ among Iranian Student in Malaysia
Bibliographic record
Abstract
The main goal of this study is to determine whether SI-G training is able to help increase SQ. The presentexperimental study examined the effect of SI-G training on spiritual intelligence among Iranian students in ImamKhomeini School situated in Kuala Lumpur. This study has evaluated the effect of SI-G training programregarding the increasing SQ and its subscales. It evaluates the follow up test and the sustainability of the trainingprogram. The study shows that with SI-G training, spiritual intelligence and its subscales can be enhanced.Essentially, spiritual intelligence is a factor that affects training, practice and society in general. There is arelationship between most of the subscales of spiritual intelligence, therefore, training some of the subscales ofspiritual intelligence can directly affect the other subscales. In this study, spiritual intelligence was measured byIntegrated Spiritual Intelligence Scale (ISIS) employed to assess students’ spiritual intelligence before and afterthe three weeks training period. Subsequent findings were discovered following SI-G training program. The resultof the pre-test showed that most of the students have low SQ and the researcher has chosen 34 of them as sample.The sample gratitude has the lowest score and run through the highest mean between other subscales. This studyrevealed the significant relationship between 22 subscales of spiritual intelligence and between these subscales inrelation to spiritual intelligence. The findings provided that SI-G training has effect on increasing spiritualintelligence and also improved most of the subscales. After 3 weeks, the researcher conducted a follow up test,comparing its result with post test, revealed that training program did not have good sustainability on SQ and someof the subscales. However, after comparing the results with pre-test, training program showed an effect onincreasing SQ within three weeks of training.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".