Needs Assessment for Enhancing Servant Leadership Among Private Educational Institution Administrators Under the Office of the Private Education Commission, Northeastern Region
Bibliographic record
Abstract
This research aimed to study the current conditions, desired conditions, and development needs for enhancing servant leadership of private school administrators under the Office of the Private Education Commission. The study employed quantitative research methodology. The research sample comprises 259 private school administrators from the Office of the Private Education Commission in the academic year 2024, selected through a multi-stage sampling process. The sample size was determined using Krejcie and Morgan’s table. The research tool was a questionnaire examining current and desired conditions using a 5-level rating scale. The statistics used for data analysis included frequency, percentage, arithmetic mean, standard deviation, and the Priority Needs Index (PNImodified). The research findings revealed that: 1) The current condition was at a moderate level, while the desired condition of school administrators’ leadership overall was at the highest level. 2) The overall (PNImodified) value was 1.54, indicating a moderate level of necessary needs requiring development. In order of priority, people development had the highest development need (PNImodified = 2.05), followed by active listening (PNImodified = 1.92), caring for others (PNImodified = 1.11), and having vision (PNImodified = 1.07), respectively. Therefore, enhancing servant leadership among private school administrators under the Office of the Private Education Commission should incorporate the development of these characteristics in training curricula.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".