P.171 A psychological assessment tool to improve quality of life in neurosurgical residents: a prospective cohort study
Bibliographic record
Abstract
Background: Neurosurgery is a long and arduous training program, and the demands of neurosurgical training have led to resident burnout prevalence ranging from 11-67%, attrition, and suicide. We aimed to assess whether implementation of a weekly self-assessment tool with optional psychological counselling improves neurosurgical resident quality of life. Methods: We performed a one year prospective cohort study including 14 Calgary (intervention group) and 12 Toronto/Winnipeg residents (control group). Calgary residents utilized a mobile application (“HONE”) weekly, and all residents responded to questionnaires at baseline, midpoint and endpoint: EQ-5D-5L, Maslach Burnout Inventory (MBI), and Mayo Clinic Well-being Index (WBI). Between and within group results were compared using two-tailed t-tests. Results: Pooled baseline scores were comparable to population norms, with increased mean MBI depersonalization scores (10.28 versus 7.12, p=0.033), and more WBI “at risk” scores compared to normative data. There were no baseline differences between cohorts. EQ-5D-5L, MBI, and WBI scores were comparable between and within cohorts at all three time points. Three intervention group residents accessed psychological counselling, totalling ten sessions. Conclusions: Weekly use of the HONE application did not impact resident quality of life, although multiple residents displayed help-seeking behaviours. HONE provided tangible data for the program director to track trends in team well-being.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".