2015 American Academy of Clinical Neuropsychology (AACN) student affairs committee survey of neuropsychology trainees
Bibliographic record
Abstract
OBJECTIVE: Surveys of practicing neuropsychologists have been conducted for years; however, there have been no comprehensive surveys of neuropsychology trainees, which may result in important issues being overlooked by the profession. This survey assessed trainees' experiences in areas such as student debt, professional development, and training satisfaction. METHOD: Survey items were written by a task force of the AACN Student Affairs Committee (SAC), and neuropsychology trainees were recruited via neuropsychology-focused listservs. In total, 344 trainees completed the survey (75% female) and included participants from every region of the US and Canada. RESULTS: Based on the survey questions, nearly half of all trainees (47%) indicated financial factors were the greatest limitation in their training. Student debt had a bimodal distribution; 32.7% had minimal debt, but 45% had debt >$100,000. In contrast, expected starting salaries were modest, but consistent with findings ($80-100,000). While almost all trainees intended to pursue board certification (97% through ABPP), many were 'not at all' or only 'somewhat' familiar with the process. CONCLUSIONS: Results indicated additional critical concerns beyond those related to debt and lack of familiarity with board certification procedures. The results will inform SAC conference programming and the profession on the current 'state of the trainees' in neuropsychology.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".