Burnout in sickle cell disease-focused hematology-oncology trained providers: A national cross-sectional study
Bibliographic record
Abstract
Abstract Introduction: Burnout is defined by emotional exhaustion, depersonalization, and reduced personal accomplishment. It affects roughly half of physicians in the United States (U.S.), leading to physician turnover, decreased productivity, and increased risk for medical errors. Burnout has been shown to affect approximately ⅓ of the U.S. hematology-oncology workforce. Burnout in U.S. hematology-oncology (Hem-Onc) trained sickle cell disease (SCD) focused providers has not been previously explored. SCD-focused providers contend with complex clinical care, high patient acuity, systemic barriers, and limited disease-modifying therapies, potentially elevating burnout risk. In our study we sought to examine the prevalence of burnout in SCD-focused providers compared to general Hem-Onc providers, hypothesizing higher burnout among SCD-focused providers. We also explored the contribution of work and personal characteristics to burnout and compared these factors between the two cohorts. Methods: We conducted a nationwide cross-sectional survey of U.S. Hem-Onc trained physicians (adult and pediatric) between September and November 2024. Respondents self-identified as SCD-focused (n = 55) or non–SCD-focused (n = 104). Burnout was measured using a validated single-item instrument (score ≥ 3 indicates burnout). We also assessed grit (Short Grit Scale, 1–5) and resilience (CD-RISC2, 0–8). In addition, other characteristics were captured including demographics, workplace characteristics (income, sense of pride in one's job, work hours, etc.) and personal characteristics (recreational activity, support system, etc). Between-group comparisons used Chi-square or Fisher's exact tests for categorical variables and t-tests or Mann–Whitney U tests for continuous variables. A bivariable logistic regression model tested for the association of recreation frequency and sense of pride and burnout in SCD-focused providers. Results: Of 159 respondents, burnout prevalence was higher among SCD-focused providers compared with non-SCD focused (60% vs 44%; p = 0.046). There was no difference in grit (median 3.4 vs 3.5; p = 0.23) or resilience (median 6 vs 6; p = 0.26) between the two groups. SCD-focused providers participated in less recreational activity per week (27% vs 51%; p = 0.006). SCD-focused providers were less likely to strongly agree with the statement “I feel a sense of pride in my job,” (47% vs 65%; p = 0.031). Other notable differences were that SCD-focused providers were more likely to practice in an academic center (89% vs 74%; p = 0.026), hold an administrative/leadership role (36% vs 16%; p = 0.004), and report a lower income (< $350,000: 64% vs 40%; p = 0.005) despite having more years in practice (> 5 years: 81% vs 63%; p = 0.016). There was no significant difference in number of hours worked per week or working during personal time. In an exploratory analysis, we found recreation and a sense of pride in one’s work to be independent negative predictors of burnout (p<0.001). In a two-variable logistic regression model, recreation and sense of pride attenuated burnout in SCD providers, indicating both as potential mediators. Conclusions: Our findings show that SCD-focused providers report higher burnout compared to non-SCD providers. However, recreational time and a sense of pride in one's job are likely mediators to burnout in SCD-focused providers. This suggests that the higher burnout rates observed in SCD-focused providers are likely driven by external systemic factors that disproportionately affect those who care for persons with SCD. The absence of difference in grit and resilience between groups further supports this conclusion. Exploratory analysis revealed that work and personal factors may contribute to this. SCD-focused providers may be concentrated in roles or systems that lead to less recreational time and expose them to increased risk for burnout. This includes caring for a complex and high-acuity population that has been historically marginalized with limited resources at the healthcare system and both state and federal government levels. Further quantitative and qualitative studies are warranted to understand factors that contribute to or are protective of burnout, and minimize burnout to strengthen the SCD workforce.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".