The profession of neuropsychologist in Canada: Findings of a national survey
Bibliographic record
Abstract
The purpose of this study was to investigate the demographic characteristics, academic training and types of professional activities of clinical neuropsychologists in Canada. 282 participants completed an online-based survey. Respondents were women for the most part and had a mean age of 43 years. They typically had doctoral-level training (85%) and about one-quarter had postdoctoral training (23%). Nearly half (47%) had a lifespan practice, over one-third (37%) had an adults-only practice, and about one-sixth (16%) had an exclusively pediatric practice. Most worked full-time (79%). Respondents were almost evenly split three ways between those who worked in the public sector, those who worked in the private sector, and those who worked in both. The most common professional activities related to assessment (95%), although clinical supervision (43%) and rehabilitation (42%) were also quite frequent, whereas research (27%) and teaching (18%) were less so. The most common reason for referral was to determine a diagnosis (79%). Pediatric neuropsychologists worked primarily with individuals with neurodevelopmental disorders and neuropsychologists working with adult populations worked primarily with individuals with emotional disorders, acquired neuropsychological disorders (traumatic brain injury, stroke/vascular), and neurocognitive disorders (dementia). At time of study, Canadian neuropsychologists seemed to enjoy a fairly balanced situation: Their level of training and the ratio of neuropsychologists per population were both high. However, these varied widely across Canada. This suggests that the profession and public interest would stand to gain from seeing training standardized to some extent nationwide.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".