“Teacher Burnout Is One of My Greatest Fears”: Interrupting a Narrative on Fire
Bibliographic record
Abstract
Teacher burnout is often positioned as a common result of the complex demands of the teaching profession (García-Carmona et al., 2019). While there is no denying the demanding nature of teaching, in this article we present an alternative perspective on the widespread burnout discussion that distinguishes between burnout and the complexities of teacher attrition, and offer a more hopeful and strengths-based approach to the teaching profession. In a qualitative study that analyzed the anticipatory beliefs that pre-service teachers expressed in a reflective assignment for a course focused on Comprehensive School Health (CSH), we found evidence to suggest that the burnout narrative may threaten teacher candidates’ self-efficacy before entering the teaching profession. We call for a disruption to the overemphasis of burnout narratives in teacher education programs as they may undermine the profession.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".