Student Teachers’ Proactive Strategies and Experienced Learning Environment for Reducing Study-Related Burnout
Bibliographic record
Abstract
The study aims to gain a better understanding of the interrelation and the development of student teachers’ proactive coping strategies, i.e., self-regulative and co-regulative strategies, perceived learning environment and study-related burnout. Longitudinal data were utilized with three annual measurements during bachelor studies. Altogether, 270 primary school student teachers completed the survey. The data was analyzed by using Structural Equation Modeling (SEM). Results showed that the self-regulative strategy adopted by student teachers promoted the use of co-regulative strategy. Co-regulative strategy use in turn contributed to the perceived fit between the student teacher and the learning environment, and further, reduced study-related burnout. Moreover, student teachers’ ability to utilize proactive self-regulative strategies to buffer potential stressors in advance, i.e., an ability to manage one’s own study pace in the direction of well-being, was effective in reducing the risk of developing burnout. Results also showed that both the key determinants for reducing study-related burnout, i.e., proactive strategies and experienced learning environment, and the study-related burnout symptoms themselves were relatively stable.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".