Indicators of Adaptive Leadership for Teachers in Boromarajonani College of Nursing Under Boromarachanok Institute: Developing and Testing the Structural Relationship Model
Bibliographic record
Abstract
Background of the study: Leadership is needed when one must deal with education management which means the relationship of influence between the leader and followers who have the same goal, to make a change. It can be used as a guideline in the planning and creating evaluation criteria to improve the adaptive leadership of teachers at Boromarajonani College of Nursing which consistent with the theory and the research result as well as the vision from scholars and organization. Objective: The objectives of this research were to (a) develop a theoretical model of structural relationship of major components, minor components and indicators of adaptive leadership, (b) test the fitness of the model was developed from theoretical model and empirical data, and (c) verify the factor loading value of the major components, minor components and indicators. Materials and Methods: The descriptive research collected by using the 5-level rating scale questionnaire. The population consisted of 1,740 teachers in Boromarajonani college of nursing under Boromarajchanok Institute. The data analysis using AMOS statistical program was done with the 617-return questionnaire. Results: The results showed that the theoretical model was consistent with empirical data with the following statistical metrics: relative Chi-square (CMIN/DF) < 3.00, root mean square error of approximation (RMSEA) < 0.05, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), and normed fit index (NFI) 0.90-1.00 were in accordance with the criteria from both the first and second order of confirmative factor analysis. The major components had factor loading ranging from 0.71 to 1.00, which is higher than the criterion as 0.70. The minor components and indicators had factor loading are higher than the criterion as 0.30. Conclusion: The finding can be used as a guideline in the planning and creation of evaluation criteria to improve the adaptive leadership of teachers at Boromarajonani College of Nursing which is consistent with the theory and research results as well as the vision from scholars and organization. This will help improve the performance efficiency of the teachers at Boromarajonani College of Nursing which will affect the education management and education improvement.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".