B - 112 Challenges and Opportunities for Training in a New Era in Neuropsychology: a Survey Study
Bibliographic record
Abstract
Abstract Objective The field of neuropsychology is undergoing significant changes, especially as the Minnesota Update Conference (MUC) drafts are developed. In late 2023, a group of neuropsychology trainee-leaders, united through the Clinical Neuropsychology Trainee Forum (CNTF), surveyed trainees in the United States and Canada to better understand their needs and perception of the neuropsychology training climate. Method Survey items were written by a CNTF task force consisting of trainee-leaders from major neuropsychology organizations before being refined by four independent neuropsychologists, including two current neuropsychology postdoctoral fellowship training directors. The survey was distributed via listservs and social media. Results 220 respondents completed a majority of the survey, and they were primarily female (86%), White (71%), and training in the United States (92%). More than 75% of respondents reported satisfaction with their doctoral program, internship, and/or fellowship. Similarly, 90% and 73% of respondents, respectively, felt their opinions on the future of the field are listened to and/or valued in discussions with peers and supervisors/mentors. However, only 57% of respondents felt their opinions are valued by neuropsychology organizational leadership, and nearly 70% were uncertain or disagreed that trainees’ opinions were valued during the MUC draft development and review period. Conclusions Most neuropsychology trainees are satisfied with their training programs yet expressed concern about their opinions on the future of the field being heard by neuropsychology leadership. Trainee stakeholder engagement will be key to the vitality of neuropsychology in light of anticipated MUC Guidelines. Recommendations for supervisors, training directors, and neuropsychology organizational leadership will be discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".