National Human Resources Survey of Clinical Neurophysiologists in Canada
Bibliographic record
Abstract
BACKGROUND: Although electromyography (EMG), electroencephalography (EEG) and evoked potential (EP) studies are common investigation tools for patients with neurologic illnesses, no formal data on the manpower supply in Canada exists. Because of the importance of these on training requirements and future planning, the purpose of this study was to establish a comprehensive profile of the human resources situation in clinical neurophysiological services across Canada. METHODS: A questionnaire was sent to all clinical neurophysiologists in Canada. To capture the maximal number of respondents, a total of three rounds of mail out were done. In addition, to obtain accurate demographic data on supporting technologists, a separate survey was also carried out by the Association of Electrophysiological Technologists of Canada. RESULTS: Of the 450 clinical neurophysiologists identified and surveyed, the provincial response rate was 59 +/- 14% (mean +/- SD). Of these, the vast majority practiced in urban centres. There was substantial regional disparity in different provinces. While the wait time for most EEG and EP laboratories was less than six weeks, the wait time for EMG was substantially longer. With the age of the largest number of practitioners in their sixth decade, projected retirement over the next 15 years was 58%. The demographic distribution of the supporting technologists showed a similar trend. CONCLUSIONS: In addition to considerable regional disparity and urban/rural divide, a large percentage of clinical neurophysiologists and supporting technologists planned to retire within the coming decade. To ensure secure and high standard services to Canadians, solutions to fill this void are urgently needed.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".