Factors associated with increased burnout in genetic counseling students
Bibliographic record
Abstract
Genetic counseling students face numerous stressors during their graduate program, which can lead to negative outcomes such as burnout. Burnout can negatively impact students' ability to perform well, maintain stamina, and feel competent throughout their training. The Maslach Burnout Inventory (MBI) is a validated survey that assesses three domains that together define burnout: exhaustion, cynicism, and low self-efficacy. One hundred eighty genetic counseling students from the classes of 2024 and 2025 within the United States and Canada completed a cross-sectional, quantitative survey which included the MBI validated for students, a list of 19 situations tailored to students in genetic counseling training programs that may cause stress, and three open-ended questions asking what activities helped reduce stress, including resources provided by the program and resources students would like to be provided. Genetic counseling students endorsed an average of nine situations that caused them "some" or "a lot" of stress. Results revealed that 12% of current students met criteria for having burnout (defined as high exhaustion, high cynicism, and low self-efficacy); an additional 20% met criteria for two of the three burnout subscales. The most consistent predictors of burnout were the numbers of situations causing a student stress and being in the latter part of training. Thematic analyses revealed that personal activities, social activities, and mental health services helped reduce stress, while support in relation to academics was a desired resource. This study reveals that burnout is experienced by genetic counseling students and is associated with factors such as having a high number of situations causing a student stress or being more than halfway through the graduate program. These results provide insight into areas and methods for genetic counseling graduate programs to attenuate burnout in their students.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".