Trainee perceptions of multicultural climate and supervision in neuropsychology
Bibliographic record
Abstract
INTRODUCTION: Mentor relationships are important in developing and supporting professional self-efficacy among psychology trainees. Additionally, the rapid diversification of the United States calls for the preparation of clinical neuropsychology trainees to work within a multicultural context. The present study aimed to assess neuropsychology trainees' perceptions of multicultural climate and supervision and if these perceptions differ based on trainee demographics. We also sought to identify aversive experiences of trainees, program strengths or weaknesses, and how programs support trainees. METHOD: = 71) programs across the United States and Canada who completed a survey assessing perceptions to multicultural climate and supervision. 64.5% self-identified as women, 60.3% as heterosexual, and 46.1% as non-Hispanic White. 34.5% of trainees reported at least one American Disabilities Act (ADA) recognized disability. RESULTS: Though satisfied with general supervision, trainees reported overall dissatisfaction with multicultural supervision. Satisfaction with multicultural supervision did not differ by demographics. Trainees also reported various aversive experiences with supervisors, clients, and research participants that negatively impacted their training. These experiences were at times due to an aspect of the trainee's multicultural identity, with Black and Hispanic trainees being more likely to report an aversive experience. Trainees reported ways in which they felt unsupported by their programs. CONCLUSIONS: Important areas of growth for programs are discussed. Issues raised by neuropsychology trainees overlap to some degree with the experiences of trainees in other fields. Recommendations of approaches that have been successfully adopted in other fields to improve trainee satisfaction are provided. Early identification of needs that go above and beyond clinical training will allow programs to respond promptly, improve trainee satisfaction, and potentially improve the retention of trainees from diverse backgrounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".