The Minnesota Conference proposed guidelines for education and training in clinical neuropsychology
Bibliographic record
Abstract
OBJECTIVE: The Houston Conference Guidelines (Hannay et al., 1998) provided an initial framework for North American neuropsychology training that served the specialty well for several decades. Subsequent advances in technology, increased diversity of the U.S. and Canadian populations, and the adoption of competency-based training models within Health Service Psychology have created a need to update neuropsychology training guidelines. Therefore, in 2022, the Minnesota Conference to Update Education and Training Guidelines in Clinical Neuropsychology began a two-year drafting process leading to the currently proposed update. METHOD: A Steering Committee worked with content experts, consultants, and delegates representing North American neuropsychological organizations and specialists. The final version of the guidelines was developed after reviewing neuropsychological training literature, gathering feedback from specialists, and making iterative revisions of earlier drafts to reach consensus. CONCLUSION: The resulting "Minnesota Guidelines" include five foundational (Neuroscience and Brain Behavior Relationships; Integration of Science and Practice; Ethics, Standards, Laws, and Policies; Diversity; and Professional Relationships) and eight functional (Assessment; Intervention; Interdisciplinary Systems and Consultation; Research and Scholarship; Technology and Innovation; Teaching, Supervision, and Mentoring; Health and Professional Advocacy; and Administration, Management, and Business) areas of competency required for entry level specialty practice. While consensus was not achieved, a majority of voting delegates recommended the Guidelines for adoption and the Guidelines have been endorsed by six neuropsychology education and board certification organizations. The American Academy of Clinical Neuropsychology has not endorsed the Minnesota Guidelines and will not make an endorsement decision until three months after online publication.
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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 | 0.102 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".