The Houston Conference on Specialty Education and Training in Clinical Neuropsychology I. Preamble for conference. Policy Statement
Bibliographic record
Abstract
Clinical neuropsychology is a specialty formally recognized by the American Psychological Association (APA) and the Canadian Psychological Association (CPA). Education and training in clinical neuropsychology has evolved along with the development of the specialty itself. Nevertheless, there has been no widely recognized and accepted description of integrated education and training in the specialty of clinical neuropsychology The aim of the Houston Conference was to advance an aspirational, integrated model of specialty training in clinical neuropsychology. The Conference Planning Committee solicited participant applications by way of an announcement in the APA Monitor and letters to members of the Division of Clinical Neuropsychology (Division 40), the National Academy of Neuropsychology (NAN), and to the directors of training programs at the doctoral, internship, and postdoctoral levels as listed in The Clinical Neuropsychologist (Cripe, 1995). The committee selected a group of 37 clinical neuropsychologists to reflect diversity in practice settings, education and training models, specializations in the field of clinical neuropsychology, levels of seniority, culture, geographic location, and sex. Five additional delegates attended as representatives of the sponsoring neuropsychological organizations (NAN; Division 40; the American Board of Clinical
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.105 | 0.061 |
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".