LEADERSHIP IN THE EDUCATIONAL ENVIRONMENT AND ITS CONSEQUENCES ON PSYCHOLOGICAL HEALTH
Bibliographic record
Abstract
Abusive supervision involves the expression of multiple verbal and nonverbal aggressive behaviours of a supervisor towards employees.As a stressor in the workplace, such leadership results in organisational inefficiency, absenteeism, and voluntary staff turnover.The cost of this supervision for American organisations is estimated at $2.3 billion (Seckyoung et al., 2016).An understanding of the predictors of abusive supervision in the workplace allows for intervention amongst organizations in order to significantly reduce the cost associated with these destructive behaviours.Empirical data shows that the perception of abusive supervision is associated with psychological distress, reduced workplace wellbeing, and low-quality supervisor-subordinate relationships.This study proposes an empirical exploration of the antecedents and consequences of abusive supervision in the education sector, which has been identified by certain studies (ACTU, 2000) as being a work environment where destructive leadership by school officials is particularly pronounced.Several variables, such as managerial overload or work intensification, the setting of imposing or unrealistic work objectives, high-performance human resource management practices or the frustration of managers facing a lack of resources can potentially predict the perception of abusive supervision.Supervisors' personality traits constitute mediating variables in this framework.The personality traits and attributions of subordinates influences the perception of abusive supervision.This study derives from a narrative literature review (1980-2020) on three keywords: abusive supervision, school management, and teachers.For this purpose, the databases PsychINFO, PubMed, ERIC (ProQuest), and Web of Science were consulted.References were sorted in the data processing software EndNote.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".