Barriers to board certification in clinical neuropsychology identified by surveyed trainees and professionals
Bibliographic record
Abstract
Objective: Board certification (BC) in clinical neuropsychology via the American Board of Clinical Neuropsychology (ABCN) is a rigorous process demonstrating clinical competence to practice. While myths about BC have been addressed, barriers to BC have yet to be studied. The aim of this study was to identify barriers to BC among neuropsychology trainees and professionals. Method: Data were collected through pre-webinar surveys administered to 1202 participants across four webinars conducted between 2018 and 2021. The surveys, via open-ended questions, captured specific concerns about BC as well as, demographic information including self-identification with racial/ethnic and culturally diverse groups. Qualitative analyses of self-reported barriers were conducted, and themes were identified. Results: The themes identified included Preparedness (11.8%), Lack of Training and Mentoring Opportunities (5.8%), Training Flexibility (11.9%), BC Knowledge (13.4%), Overall Knowledge of neuropsychology (4.4%), Time (24.7%), Money (10.9%), Documentation (3.4%), International Issues (1.5%), and COVID-19 concerns (2.5%). Respondents that identified with a racial/ethnic diverse group were more likely to report Opportunities and International Issues, whereas White respondents more frequently identified Time and Documentation as barriers. Trainees were more likely to report Training Flexibility, Opportunities, BC Knowledge, whereas Professionals were more likely to report Preparedness and Time as barriers. Conclusions: Results from this survey demonstrate that Time, BC Knowledge, Training Flexibility, Preparedness, and Money related to the examination were the most frequently reported barriers. However, differences across groups (i.e. career stage, racial/ethnic) emerged, highlighting the need to develop initiatives that address the specific needs of different groups of neuropsychology trainees and professionals.
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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 | 0.009 | 0.042 |
| 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 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".