Demographics, Interests, and Quality of Life of Canadian Neurosurgery Residents
Bibliographic record
Abstract
BACKGROUND: Neurosurgical residents face a unique combination of challenges, including long duty hours, technically challenging cases, and uncertain employment prospects. We sought to assess the demographics, interests, career goals, self-rated happiness, and overall well-being of Canadian neurosurgery residents. METHODS: A cross-sectional survey was developed and sent through the Canadian Neurosurgery Research Collaborative to every resident enrolled in a Canadian neurosurgery program as of April 1, 2016. RESULTS: We analyzed 76 completed surveys of 146 eligible residents (52% response rate). The median age was 29 years, with 76% of respondents being males. The most popular subspecialties of interest for fellowship were spine, oncology, and open vascular neurosurgery. The most frequent self-reported number of worked hours per week was the 80- to 89-hour range. The majority of respondents reported a high level of happiness as well as stress. Sense of accomplishment and fatigue were reported as average to high and overall quality of life was low for 19%, average for 49%, and high for 32%. Satisfaction with work-life balance was average for 44% of respondents and was the only tested domain in which significant dissatisfaction was identified (18%). Overall, respondents were highly satisfied with their choice of specialty, choice of program, surgical exposure, and work environment; however, intimidation was reported in 36% of respondents and depression by 17%. CONCLUSIONS: Despite a challenging residency and high workload, the majority of Canadian neurosurgery residents are happy and satisfied with their choice of specialty and program. However, work-life balance, employability, resident intimidation, and depression were identified as areas of active concern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".