The Relationship Between Burnout and Occupational Stress in Genetic Counselors
Bibliographic record
Abstract
Burnout represents a critical disruption in an individual's relationship with work, resulting in a state of exhaustion in which one's occupational value and capacity to perform are questioned. Burnout can negatively affect an individual's personal life, as well as employers in terms of decreased work quality, patient/client satisfaction, and employee retention. Occupational stress is a known contributor to burnout and occurs as a result of employment requirements and factors intrinsic to the work environment. Empirical research examining genetic counselor-specific burnout is limited; however, existing data suggests that genetic counselors are at increased risk for burnout. To investigate the relationship between occupational stress and burnout in genetic counselors, we administered an online survey to members of three genetic counselor professional organizations. Validated measures included the Maslach Burnout Inventory-General Survey (an instrument measuring burnout on three subscales: exhaustion, cynicism, and professional efficacy) and the Occupational Stress Inventory-Revised (an instrument measuring occupational stress on 14 subscales). Of the 353 respondents, more than 40 % had either considered leaving or left their job role due to burnout. Multiple regression analysis yielded significant predictors for burnout risk. The identified sets of predictors account for approximately 59 % of the variance in exhaustion, 58 % of the variance in cynicism, and 43 % of the variance in professional efficacy. Our data confirm that a significant number of genetic counselors experience burnout and that burnout is correlated with specific aspects of occupational stress. Based on these findings, practice and research recommendations are presented.
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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 | 0.002 | 0.014 |
| 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) | 0.001 | 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 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".