Focus teacher interventions on preventing and ameliorating burnout symptoms rather than raising TSE levels¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 2 studies (1 review, 1 associational), q2–q3 · 1 of 2 report an effect size · 4 claims rest on one study

Description¶

Because burnout dimensions consistently predicted future TSE while TSE did not consistently predict future burnout, the article recommends that interventions aiming to support teacher retention and well-being target burnout. It states that "strategies may need to focus on preventing and/or ameliorating the burnout symptoms rather than raising TSE levels," warning that deploying strategies to increase TSE may have minimal effects. Suggested multi-level approaches include cognitive emotion regulation strategies for teachers, school-level factors such as collegial leadership and preparedness, and professional knowledge and classroom management training in teacher education.

Design Implications¶

Context¶

Requirements¶

• Interventions should address factors associated with burnout, especially when teachers are showing or beginning to show symptoms of burnout

Constraints¶

• The recommendation rests on longitudinal evidence from a Croatian teacher sample; the authors call for replication in other cultures

Target Learners¶

• pre-service and in-service teachers

Target Learning Goals¶

• teacher well-being, burnout prevention, teacher retention

Claims¶

• Burnout dimensions (exhaustion and disengagement) negatively predict future teacher self-efficacy at adjacent time points [+W]
• TSE does not consistently predict future burnout; only one significant path (TSE to disengagement, Time 1 to Time 2) emerged [+W]
• TSE is negatively correlated with both exhaustion and disengagement at each of the three time points [+W]
• Teacher self-efficacy shows negative associations with burnout factors [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Kim, Lisa E. and Burić, Irena. (2020). Teacher self-efficacy and burnout: Determining the directions of prediction through an autoregressive cross-lagged panel model. Journal of Educational Psychology. https://doi.org/10.1037/edu0000424