Resilience, Psychological Distress, and Academic Burnout among Accounting Students*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This study's objective is to examine the role of resilience in the dynamic between academic burnout and psychological distress using a sample of US undergraduate accounting majors. It extends prior research—that is, García‐Izquierdo et al. (2018), who examine these relationships using a sample of Spanish nursing students. For this study, a survey instrument was concurrently administered to 443 accounting majors at four geographically dispersed universities. Two alternative models are tested. The first model positions resilience as an exogenous predictor, and dimensions of academic burnout antecedent to psychological distress. The results indicate a significant negative association between resilience, psychological distress, and each of the three academic burnout dimensions. In addition, emotional exhaustion and academic inefficacy have a significant positive association with psychological distress. The alternative model positions psychological distress antecedent to each of the academic burnout dimensions. The results indicate that resilience has a significant negative association with psychological distress, cynicism, and academic inefficacy, but not emotional exhaustion. Moreover, psychological distress has significant positive associations with each academic burnout dimension. In the alternative model specification, resilience is also found to moderate the association between psychological distress and academic inefficacy. This single moderating effect notwithstanding, the findings suggest that the primary role of resilience is that of a compensatory mechanism by acting as an independent exogenous predictor of distress and burnout.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it