The incremental association of implementation leadership and school personnel burnout beyond transformational leadership.
Bibliographic record
Abstract
Successful implementation of school-wide interventions (i.e., delivered to all students by a wide array of school personnel) is key to promoting students' academic achievement and psychosocial development. Yet, the implementation of school-wide interventions is complex and can be psychologically taxing for implementing personnel. If evidence-based practice and program (EBP) implementation goes unsupported, implementation challenges might result in chronic stress among school personnel that leads to burnout. While generally effective leadership tends to decrease educator burnout, implementation-specific leadership may also decrease burnout through its strategic supports for EBP implementation. A series of linear regression and path models were used to examine the concurrent association between transformational (e.g., general) and implementation (e.g., strategic) leadership and burnout and its component parts (i.e., emotional exhaustion, depersonalization, personal accomplishment). In a sample of 338 school personnel, we found transformational and implementation leadership were each significantly associated with decreased burnout. However, transformational leadership was not significantly associated with any of the three burnout components, whereas implementation leadership was significantly associated with increased personal accomplishment. These results suggest both general and strategic forms of leadership are key supports for school personnel burnout and as such, leaders may benefit from training to improve each. Additional implications for schools and future directions to understand how best to support school personnel are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.004 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".