P.106 Ethics education in neurosurgical training- a survey of North American program directors
Bibliographic record
Abstract
Background: Despite being mandatory for accreditation by the RCPSC and ACGME, little is known about how ethics education is undertaken during neurosurgery training. This study assessed the current state of ethics education in North American neurosurgery training programs. Methods: A web-based survey was developed based on ethics competencies outlined by the RCPSC and the ACGME and emailed to North American neurosurgery residency program directors(PD’s). Responses were analyzed using descriptive statistics. Results: 47/119 (40%) PD’s completed the survey. Most(74%) spent <10 hours/year on ethics education. Informal discussion(86%), case presentations(67%) and lectures(55%) were common teaching methods. Most(85%) felt real-life experience was the best teaching method. Neurosurgical faculty(86% of programs), medical faculty(48%) and ethicists(26%) provided ethics teaching. Time constraints(42%) and lack of expert faculty(24%) were common barriers. Important topics were end of life care(95%), conflicts of interest(81%), informed consent(81%), futility(66%) and research ethics(66%). Most(78%) felt ethics education should be mandatory and that trainees were prepared to deal with ethically challenging situations(95%). Conclusions: This study provides a snapshot of ethics education in neurosurgery training. Time constraints and a lack of expert faculty were seen as barriers to ethics education. Most program directors felt residents were well prepared to deal with ethical issues. Identified ethical topics of importance should be incorporated into training curricula.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".