The Impact of Burnout on the Academic Achievement of Saudi Female Students Enrolled in the Colleges of Health Sciences
Bibliographic record
Abstract
Objectives: The objectives of this study were to investigate the impact of burnout on the academic achievement of female students in the colleges of health sciences and to explore on their educational levels of study regarding burnout.Methods: Purposive sampling technique was used in this study and a sample size of 540 Saudi female students was chosen to participate in this study. The tools used were Maslach Burnout Inventory–General Survey for Students MBI–GS (S) to measure the level of burnout and a socio-demographic questionnaire were administered on participants. The MBI–GS (S) proved internal consistency reliability at 0.86 across many samples.Results: The level of burnout among participants was 51.9%. Some 27.2% of the participants experienced mild burnout, while 14.4% showed moderate levels of burnout. Only 6.5% of participants displayed a high level of burnout, and they were among the Colleges of Pharmacy (25.0%) and Dentistry (10.1%).Conclusions: The study findings showed no association between burnout and academic achievement and no relationship between burnout and the academic level of study. Only the students of the College of Pharmacy suffered from high levels of burnout.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".