Bibliographic record
Abstract
BACKGROUND: Medical students in Canada must make career choices by their final year of medical school. Selection of students for a career in neurosurgery has traditionally been based on marks, reference letters and personal interviews. Studies have shown that marks alone are not accurate predictors of success in medical practice; personal skills and attributes which can best be assessed by letters of reference and interviews may be more important. This study was an attempt to assess the importance of, and ability to teach, personal skills and attitudes necessary for successful completion of a neurosurgical training program. METHODS: A questionnaire was sent to 185 active members of the Canadian Neurosurgical Society, asking them to give a numerical rating of the importance of 22 personal skills and attributes, and their ability to teach those skills and attributes. They were asked to list any additional skills or attributes considered important, and rate their ability to teach them. RESULTS: Sixty-six (36%) questionnaires were returned. Honesty, motivation, willingness to learn, ability to problem solve, and ability to handle stress were the five most important characteristics identified. Neurosurgeons thought they could teach problem solving, willingness to consult informed sources, critical thinking, manual dexterity, and communication skills, but honesty, motivation, willingness to learn and ability to handle stress were difficult or impossible to teach. CONCLUSIONS: Honesty, motivation, willingness to learn, ability to problem solve and handle stress are important for success in a neurosurgical career. This information should be transmitted to medical students at "Career Day" venues. Structuring letters of reference and interviews to assess personal skills and attributes will be important, as those that can't be taught should be present before the start of training.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".