Professional Practices, Beliefs, and Incomes of Postdoctoral Trainees: The AACN, NAN, SCN 2020 Practice and ‘Salary Survey’
Bibliographic record
Abstract
OBJECTIVE: Within a portion of the 2020 professional practice and "salary survey," to update key information regarding neuropsychology postdoctoral trainees. METHODS: Postdoctoral trainees were contacted via a variety of membership listings, including the listserv used by the program directors of the Association of Postdoctoral Programs in Clinical Neuropsychology (APPCN). Invitations sent in multiple waves to members of numerous neuropsychological organizations via e-messages and physical postcards included the request that postdoctoral trainees participate. The survey website was opened on January 17, 2020 and closed on April 2, 2020, during which time a total of 178 postdoctoral trainees in the USA and 3 in Canada participated. RESULTS: Response rate was estimated to be 56.4%, which adequately represents the target sample. The modal postdoctoral trainee is a woman whose internship was American Psychological Association (APA)-accredited and whose postdoctoral training is in an APPCN program that adheres to Houston Conference training guidelines. Extensive clinical experiences in neuropsychology in the form of externship practica and during internship were reported by the majority of trainees prior to postdoctoral training. There are few differences between APPCN and non-APPCN trainees and reported training experiences. Job satisfaction is high. Salaries appear to have increased substantially in recent years. There is universal interest in pursuing board certification. Support for the empirical foundations justifying assessment of response validity is high. CONCLUSIONS: Surveys of postdoctoral trainees continue to provide valuable perspectives regarding training background, clinical experiences, practice beliefs, and incomes of individuals who will soon launch their careers in clinical 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".