The prevalence and effect of burnout on graduate healthcare students
Bibliographic record
Abstract
Burnout is a growing epidemic among professional healthcare students. Unaddressed burnout has been shown to have psychological and performance related detriments. The purpose of this scoping literature review was to investigate the prevalence of burnout and its effects on the psychological, professional, empathetic ability, and academic acuity of graduate healthcare students. Inclusion criteria included English language papers published within the last 10 years and subjects in graduate healthcare professional programs. This search encompassed 8,214 articles. After title and abstract screening, 127 articles remained and were sorted into five domains of interest: etiology, professionalism, mental health, empathy, and academic performance. After duplicates were removed, 27 articles remained for the scoping review. Graduate level healthcare students had higher levels of burnout than age matched peers and the general population. The high prevalence of burnout within graduate healthcare students can have an effect on their mental health, empathy, and professional conduct. Understanding the occurrence and effects of burnout within graduate healthcare programs allows faculty and administration to plan curriculum, and provide information to students to understand, recognize, and create opportunities to decrease burnout in order to create long lasting quality clinicians.
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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 | 0.013 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".