Assessment of General Surgery Resident Wellness from the Perspectives of Family, Friends, and Loved Ones
Bibliographic record
Abstract
Introduction: Surgical trainees have high rates of burnout compared to residents from other specialties. However, burnout is underreported by trainees, limiting potential interventions to improve wellness. Loved ones are an underused resource for assessing wellness and detecting burnout among residents. The purpose of this study is to assess the perceptions and concerns regarding resident wellness and burnout, as well as strategies to improve wellness, from the perspective of loved ones. Methods: This cross-sectional survey study was conducted in 2022 at an urban academic center after ethics board approval. An anonymous 18-question survey to assess resident burnout, wellness, and strategies to improve wellness was distributed to loved ones of general surgery residents. Open-ended questions were analyzed using content analysis while descriptive analysis was used for Likert scale and multiple choices questions. Results: Of the general surgery residency cohort, 40.6% (13/32) of residents participated in the project, and 32 unique survey responses were received from loved ones. 73.12% of participants indicated that they were worried about the wellness of the resident. 93.75% of respondents described the resident as experiencing burnout at least once per year. Respondents reported factors most frequently contributing to resident burnout: lack of sleep (96.9%), feeling overworked or having long hours (96.9%), insufficient time for professional or academic development due to service obligations (87.1%), and feeling underappreciated (87.1%). Respondents identified the following strategies as potentially effective in improving resident wellness: more sleep/improved quality of sleep (100%), increased vacation time (96.9%), peer support and/or faculty-resident mentorship (41.9%) and wellness-focused retreats (51.6%). Conclusion: This study demonstrates that loved ones can be a valuable resource to assess resident wellness. Additionally, wellness programming should be mindful of the potential benefits of supporting basic needs such as sleep. Future projects could focus on interventions aimed at giving loved ones tools to support their wellness assessments and interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".