Developmental heterogeneity of school burnout across the transition from upper secondary school to higher education: A 9-year follow-up study
Bibliographic record
Abstract
This study utilized piecewise linear growth mixture analysis to examine the developmental heterogeneity of school burnout among a sample of 513 (67.6% females) Finnish students as they transitioned from upper secondary school to higher education (ages 17-25 years). Encompassing five measurement points (two before the transition and three after), our results revealed four distinct burnout trajectory profiles, including (a) High and Decreasing (Profile 1), (b) Moderate and Decreasing (Profile 2), (c) Low and Increasing (Profile 3), and (d) Low and Stable (Profile 4). High initial levels of self-esteem and mastery-extrinsic goals served as personal resources and high-performance goals served as personal risk factors, making students more likely to belong to more (i.e., Profile 4) or less (e.g., Profile 1) adaptive profiles of burnout trajectories, respectively. Profile 4 displayed the lowest and most stable levels of burnout, thus protecting students from adverse outcomes like school dropout, underachievement, and substance use. Conversely, Profile 1 displayed the highest and least stable levels of burnout and was associated with higher risk of burnout, lower academic achievement, greater alcohol use and problems, and higher drug use relative to the other trajectory profiles. Together, these findings offer novel person-centered, longitudinal insight into the developmental heterogeneity of burnout across the transition to higher education and lend support for the self-equilibrium hypothesis in the context of school burnout. Importantly, our results underscore the importance of early intervention efforts aimed at increasing mastery goals and self-esteem to prevent burnout and its associated consequences.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".