The Development of Training Package for Enhancing Transformational Leadership of Thai Buddhist University Students
Bibliographic record
Abstract
The aims of this study were to: 1) assess the current and expected conditions of transformational leadership of Thai Buddhist university students; 2) develop and examine the effectiveness of training package to enhance the transformational leadership of Thai Buddhist university students, and 3) study the effectiveness of the training package. This research involved 478 informants and representative samples, including executives, instructors, students, experts, and stakeholders. The research instruments were semi-structured interviews and opinion questionnaires to assess the need for transformational leadership in the current and expected conditions. These were measured using a 5-level Likert rating scale. The current condition had a reliability of 0.949, the expected condition had a reliability of 0.950, and the overall reliability was 0.974. The quality assessment form of the training package included focus group discussions, the training package, the transformational leadership assessment form, the training participation behavior assessment form, and the satisfaction assessment form. The statistics used for data analysis were frequency, percentage, mean score, standard deviation, the priority index of needs (PNI modified), and dependent t-test. The research revealed that 1) The assessment of the needs of transformational leadership of Thai Buddhist university students found that consideration of individuality was the most needed aspect (PNI modified equal to .3441), followed by intellectual stimulation (PNI modified equal to .3440), inspiration (PNI modified equal to .3398), and ideological influence (PNI modified equal to .3364), respectively. 2) The creation of the training package thethrough an online group discussion process (Zoom) showed that all activities were appropriate. 3) The study of the effectiveness of the training package found that the average score of transformational leadership of the students was significantly higher than before training at a statistical level of .05. Additionally, the average scores for behavior and participation in training and satisfaction with the training were significantly higher than the specified criterion (3.51) at a statistical significance level of .05.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".