A survey of practicum training practices in clinical neuropsychology
Bibliographic record
Abstract
Objective: While previous survey research has focused on various training constituencies such as trainees, and postdoctoral/internship supervisors, no previous survey research has examined the needs and perspectives of practicum level supervisors and training programs. This study was designed to address this limitation. These results were used in forming the North American Association of Practicum Sites in Neuropsychology (NAPSN). Method: A 41-item survey was developed and distributed to practicum supervisors across the United States and Canada via listserv. A total of 142 supervisors completed the survey; the majority were employed at academic medical centers (62%) and located in the U.S. (93%). Most participants evaluated adults (61%), while a minority evaluated children (27%), or patients across the lifespan (12%). Results: Most respondents supervise one (44%) or two (26%) students per year and prefer advanced trainees (>90%). The majority (78%) indicated students see one case per week. The number of clinical contact hours and reports varied based on location (U.S. vs. Canada) and population (adult vs. pediatric). Supervisors found professional papers and publicly available didactics to be the most helpful resources. Commonly endorsed needs included help with policies and procedures (53%), supervising trainees from underrepresented groups (49%) and nontraditional backgrounds (44%), and access to didactics (49%). Conclusions: The survey provided insights into current practices and the needs of practicum supervisors. These findings will inform NAPSN’s development of materials and policies to support trainees and supervisors and can guide other professionals in supporting their practicum trainees as they navigate training 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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".