Moving Forward: Lessons From Abroad and the Need for a Unified Approach to Clinical Neuropsychology Training in Canada
Bibliographic record
Abstract
Clinical neuropsychology as a field was fundamentally informed by Canadians almost a century ago and has seen significant growth over the past twenty years globally. While doctoral-level training has been the most common and expected training route to become a clinical neuropsychologist in Canada, there is no current uniform pathway. With regulatory changes in the practice of clinical psychology occurring across Canada, there is no consensus or standard on the regulation and training of clinical neuropsychologists. Different models of clinical neuropsychology training are summarized from around the world as well as the status of clinical (neuro)psychologists within the Canadian health care system. The challenges present in practice-based training models inherent with master’s degree training are outlined for the Canadian context, including lack of regulatory structure and supervisor availability. Overall, this paper argues for the need for a unified Canadian clinical neuropsychology training pathway for both educational and regulatory bodies that be used for future generations of trainees and trainers, and ease movement of practitioners across the country and globally.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".