School principal’s training programs, challenges, and improvement opportunities: rapid review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective school leaders, with relevant training programs, high-quality management, and in-service pedagogical training, are recognized for their ability to positively influence student performance (Sanfo, 2020). In this study, we focus on analyzing training programs for school principals, assessing aspects such as their strengths, shortcomings, opportunities, as well as potential challenges. The aim was to identify the most effective models for training and preparing school principals in order to optimize their impact on educational success. To this end, we conducted a rapid review of 27 articles from scientific and gray literature. The results of this rapid review will be discussed with a view to an in-depth reflection on the strengths, challenges and opportunities inherent in the various training methods. The analysis shows that school principals’ training is vital in the sense that it prepares trainees for their demanding and increasingly complex future roles. However, these programs sometimes suffer from shortcomings related to the selection process, the consistency between what is taught and what is experienced in the field, and the incoherence of the content of the training curriculum. The analysis also highlighted some opportunities that could improve these programs if integrated, as well as factors that could be barriers to the correct implementation of these valuable training programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it