How School Leaders Retain Experienced and Capable Teacher Mentors
Bibliographic record
Abstract
Purposes: Teacher turnover has especially negative effects on schools and students when experienced and capable teachers leave. This loss is significant when those teachers also serve as mentors to their less experienced colleagues. This study aimed to advance understanding about how school leaders can positively influence the retention of their school’s teacher mentors. Methodology: The framework for the study reflects a longstanding line of research on successful leadership. Using a cross-sectional research design, evidence was provided by responses to the mentor survey component of a larger four-year study examining the effects on retention decisions of a national induction programme for early-career teachers and their mentors in England. Structural equation modelling was employed to test the direct and indirect effects of school leadership and selected school conditions on mentors’ self-efficacy, well-being and job satisfaction, and ultimately retention decisions. Findings: Developing and retaining teacher mentors was associated with a suite of leadership practices which encourage collaborative cultures, provide coherent high-quality learning opportunities, and ensure what they perceive to be manageable workloads. These organizational conditions nurture the job satisfaction and self-efficacy of experienced teachers enhancing their sense of well-being at work. Implications: Results suggest four sets of guidelines for senior school leaders.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".