Gender and ethnic/racial diversity in clinical neuropsychology: Updates from the AACN, NAN, SCN 2020 practice and “salary survey”
Bibliographic record
Abstract
OBJECTIVE: Examination of gender and diversity issues within clinical neuropsychology, using data from the 2020 professional practice and "salary survey." METHODS: Clinical neuropsychologists in the U.S. and Canada were invited to participate in an online survey. The final sample consisted of 1677 doctoral-level practitioners. RESULTS: Approximately, 60% of responding neuropsychologists are women and 53.8% of those women identify as early career psychologists (ECPs). Conversely, a majority of men in the sample are advanced career psychologists (ACPs). Both genders work predominantly in institutions, but more men than women work in private practice. ACP men produce a greater number of peer-reviewed publications and conference presentations. Across all work settings, women earn significantly less than men, and are less satisfied with their incomes. Establishing and maintaining family life is the biggest obstacle to attaining greater income and job satisfaction for both genders. Ethnic/racial minority status was identified in 12.9% of respondents, with 59.2% being ECPs. Job satisfaction and hostility in the workplace vary across ethnic/racial minority groups. Hispanic/Latino(a) and White neuropsychologists report higher incomes, but there were no statistically significant differences between any of the groups. CONCLUSIONS: Income and select practice differences persist between female and male neuropsychologists. There is a slow rate of increased ethnic/racial diversity over time, which is much more apparent among early career practitioners. Trajectories and demographics suggest that the gender income gap is unlikely to be improved by the next survey iteration in 2025, whereas it is very likely that ethnic/racial diversity will continue to increase gradually.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".