The Role of Training, Democratization, and Self-Actualization in Addressing Employee Burnout
Bibliographic record
Abstract
The objective of this study is to investigate in depth the factors that can reduce the effect of employee burnout in Egypt. From the literature review, the variables of perception of employee development programs (IV1), time spent on employee development programs (IV2), self-actualization (IV3), and workplace democratization (IV4) were identified. To study these variables on employee burnout, SEKEM, a company in Egypt known for its innovative application of human development initiatives, was selected as a case study from Egypt. A single cross-sectional analysis of the employees of the company was used and data was collected with a questionnaire using a 7-level scale.The results were then analyzed with a principal component analysis, Cronbach’s alpha, and Spearman’s rank correlation.The results confirmed the validity and inter-reliability of the model as well as showed the significant negative relationship between both IV1 & IV3 and between employee burnout. IV2 and IV4 were not found to be significantly related to employee burnout.The significance of the research is that few studies in Egypt are made on the issue of employee burnout, and the study of SEKEM provides a rare insight into the application of such concepts.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".