Predicting Academic Burnout Based on Loneliness, Distress Tolerance, and Alexithymia in Students
Bibliographic record
Abstract
Background: Academic burnout, characterized by emotional exhaustion, cynicism towards studies, and reduced academic accomplishment, undermines students’ mental health and educational success, posing a critical challenge to their well-being. Objectives: The present study aimed to determine the extent to which loneliness, distress tolerance, and alexithymia predict academic burnout among students. Methods: This descriptive-correlational study drew its statistical population from all high school students in Shiraz during the 2023 - 2024 academic year. A sample of 259 participants was recruited via convenience sampling. Data were collected using the School Burnout Inventory (SBI), the UCLA Loneliness Scale (ULS), the Distress Tolerance Scale (DTS), and the Toronto Alexithymia Scale (TAS-20). Pearson’s correlation coefficient and simultaneous regression analysis were employed to analyze the obtained data. Results: Findings revealed significant positive correlations between loneliness (r = 0.36) and academic burnout, and between alexithymia (r = 0.48) and academic burnout. A significant negative correlation was observed between distress tolerance (r = -0.41) and academic burnout (P < 0.001). In regression analysis, loneliness, distress tolerance, and alexithymia uniquely accounted for 22%, 27%, and 28% of the variance in academic burnout, respectively, with the model explaining 28% of the total variance. Conclusions: Loneliness and alexithymia positively predict academic burnout, while distress tolerance serves as a protective factor, collectively explaining a notable portion of burnout variance in high school students. These findings highlight the need for interventions targeting social connection, emotional regulation, and distress management to mitigate burnout. Educators and policymakers can leverage these insights to develop programs fostering emotional resilience and reducing isolation among adolescents.
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 | 0.001 | 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) | 0.002 | 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".