Bibliographic record
Abstract
OBJECTIVE: This invited paper provides information about professional neuropsychology issues in Canada and is part of a special issue addressing international perspectives on education, training, and practice in clinical neuropsychology. METHOD: Information was gathered from literature searches and personal communication with other neuropsychologists in Canada. RESULTS: Canada has a rich neuropsychological history. Neuropsychologists typically have doctoral-level education including relevant coursework and supervised practical experience. Licensure requirements vary across the 10 provinces and there are regional differences in salary. While training at the graduate and internship level mirrors that of our American colleagues, completion of a two-year postdoctoral fellowship in neuropsychology is not required to obtain employment in many settings and there are few postdoctoral training programs in this country. The majority of neuropsychologists are employed in institutional settings (e.g. hospitals, universities, rehabilitation facilities), with a growing number entering private practice or other settings. There are challenges in providing neuropsychological services to the diverse Canadian population and a need for assessment measures and normative data in multiple languages. CONCLUSIONS: Canadian neuropsychologists face important challenges in defining ourselves as distinct from other professions and other psychologists, in maintaining funding for high-quality training and research, in establishing neuropsychology-specific training and practice standards at the provincial or national level, and ensuring the clinical care that we provide is efficient and effective in meeting the needs of our patient populations and consumers, both within and outside of the publically funded health care system.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".