Current state of headache training within Canadian Neurology Residency program: a national survey
Bibliographic record
Abstract
BACKGROUND: Headache disorders are the most common neurological disorders worldwide. Despite their widespread prevalence and importance, the topic of headache is inconsistently taught at both the undergraduate and postgraduate levels. The goal of this study is to establish a better picture of the current state of Headache Medicine (HM) training in Neurology postgraduate programs in Canada and describe the impact of the current pandemic on training in this domain. METHODS: Online surveys were sent to senior residents of adult Neurology programs in Canada. We also conducted telephone interviews with Neurology Program Directors. Descriptive statistics were analyzed, and thematic analysis was used to review free text. RESULTS: A total of 36 residents, and 3 Program Directors participated in the study. Most of the teaching in HM is done by headache specialists and general neurology faculty. Formal teaching is mainly given during academic half day. Most of the programs expose their residents to Onabotulinum toxin A injections and peripheral nerve blocks, but they don't offer much formal teaching regarding these procedures. Residents consider HM teaching important and they would like to have more. They don't feel comfortable performing interventional headache treatments, despite feeling this should be part of the skillset of a general neurologist. CONCLUSION: Our study is the first to establish the current state of headache teaching in post-graduate neurology programs as perceived by trainees and program directors in Canada. The current educational offerings leave residents feeling poorly prepared to manage headaches, including procedural interventions. There is a need to diversify the source of teaching, so the educational burden doesn't lie mostly upon Headache specialists who are already in short supply. Neurology Residency programs need to adapt their curriculum to face the current need in HM.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".