Primary Language and Cultural Background as Factors in Resident Burnout in Medical Specialties: A Study in a Bilingual US City
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify the degree of burnout among resident physicians enrolled in seven postgraduate training programs at Texas Tech University Health Sciences Center (TTUHSC), Paul L. Foster School of Medicine, El Paso, Texas, as it related to residents' age, gender, marital status, number of hours worked per week, primary language, race/ethnicity, and cultural background. METHOD: : The Maslach Burnout Inventory Human Service Survey (MBI) was administered to measure the level of burnout according to the prevalence of emotional exhaustion (EE), depersonalization (DP), and reduced personal accomplishment (PA). RESULTS: : Eighty-one percent of the residents at TTUHSC participated in the study. Residents raised in the United States or Canada comprised 28% and 35% of the study, and all reported English as their primary language. The EE scale was significant for obstetrics/gynecology (OB/GYN) residents (prevalence odds ratio [POR] = 13.55, P = 0.02) and psychiatry (PSY) residents (POR = 6.50, P = 0.03). Emergency medicine (EM) residents (POR = 23.35, P = 0.002), OB/GYN (POR = 10.89, P = 0.02), and general surgery (GS) (POR = 6.24, P = 0.03) residents had high DP. Internal medicine (IM) residents (primarily Spanish-speaking) reported significantly low EE (POR = 0.22, P = 0.03) and PA (POR = 0.09, P = 0.001) scores. Residents from the United States or Canada who reported English as their primary language and noted their race as white, had high EE (POR = 3.06, P = 0.03; POR = 5.61, P = 0.0001; POR = 2.91, P = 0.004), DP (POR = 3.19, P = 0.02; POR = 8.34, P < or = 0.0001; POR = 4.70, P < or = 0.0001) and PA (POR = 2.61, P = 0.02; POR = 2.35, P = 0.05, POR 0.29, P = 0.3) scores. CONCLUSION: Using valid measures, this pilot study identified a statistically significant relationship between burnout and residents' race/ethnicity, primary language, and cultural background. Larger studies with similar focus would be necessary to generalize these findings. At-risk residents in bilingual locations should be provided with cultural awareness workshops, language assistance programs, as well as senior resident and faculty mentors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".