Contributors to and consequences of burnout among clinical genetic counselors in the United States
Bibliographic record
Abstract
Prior research has found that many genetic counselors (GCs) experience burnout. Studies of other clinicians have demonstrated that burnout can have significant detrimental consequences for clinicians, patients, and the healthcare system. We sought to explore the prevalence of, contributors to, and consequences of burnout among GCs. We performed a secondary data analysis of baseline data from Me-GC, a randomized controlled trial of meditation for GCs. We applied a systems model of burnout proposed by the National Academy of Medicine (NAM), which depicts burnout arising from a combination of contributors that include both work system and individual mediating factors, and then leading to consequences. Validated self-report scales were used to measure burnout and most contributors and consequences. Female and white GCs were over-represented in our sample. Over half (57.2%) of the 397 participants had Professional Fulfillment Index scores indicative of burnout. Multiple potential contributors were associated with burnout, consistent with its known multifactorial nature. Among work system factors, higher levels of burnout were associated with insufficient administrative support, lack of autonomy, and not feeling valued by non-GC colleagues. Individual mediating factors associated with greater burnout included higher levels of anxiety, depression, and stress. Participants with lower levels of burnout reported greater mindfulness, resilience, and use of professional self-care behaviors. Among variables categorized as consequences, higher levels of burnout were associated with lower levels of empathy, counseling alliance, and positive unconditional regard, as well as higher reactive distress, and a greater desire to reduce the amount of time spent on clinical care. Given the prevalence and potential consequences of burnout observed here, it is imperative that the field take steps to mitigate burnout risk.
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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 | 0.004 |
| 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 | 0.000 | 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".