Current Educational Issues in the Clinical Neurosciences
Bibliographic record
Abstract
OBJECTIVE: Canadian training in the clinical neurosciences, neurology and neurosurgery, faces significant challenges. New balances are being set by residents, their associations and the Royal College of Physicians and Surgeons of Canada between clinical service, education and personal time. The nature of hospital-provided medical service has changed significantly over the past decade, impacting importantly on resident training. Finally, future manpower needs are of concern, especially in the field of neurosurgery, where it appears that soon more specialists will be trained than can be absorbed into the Canadian health care system. METHODS: A special symposium on current challenges in clinical neuroscience training was held at the Canadian Congress of Neurological Sciences in June 2000. Representatives from the Canadian Association of Interns and Residents, the Royal College of Physicians and Surgeons of Canada and English and French neurology and neurosurgery training programs made presentations, which are summarized in this report. RESULTS: Residency training has become less service-oriented, and this trend will continue. In order to manage the increasingly sophisticated hospital services of neurology and neurosurgery, resident-alternatives in the form of physician "moonlighters" or more permanent hospital-based clinicians or "hospitalists" will be necessary in order to operate major neuroclinical units. Health authorities and hospitals will need to recognize and assume this responsibility. As clinical experience diminishes during residency training, inevitably so will the concept of the fully competent "generalist" at the end of specialty training. Additional subspecialty training is being increasingly sought by graduates, particularly in neurosurgery. CONCLUSIONS: Training in neurology and neurosurgery, as in all medical specialties, has changed significantly in recent years and continues to change. Programs and hospitals need to adapt to these changes in order to ensure the production of fully qualified specialists in neurology and neurosurgery and the provision of optimal care to patients in clinical teaching units.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".