Survey of internship training in rehabilitation psychology: 2019.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: The purpose of this study was to obtain information about psychology internship training programs involving work with individuals with disabilities receiving rehabilitation services in the United States and Canada. RESEARCH METHOD/DESIGN: The Association of Psychology Postdoctoral and Internship Centers (APPIC) directory was used to identify 426 training programs that listed supervised experience in rehabilitation psychology, and these programs were sent a survey assessing characteristics of their internship. There were 227 program directors who responded (53%), and 114 of them reported that their internship involved working with disabled persons receiving rehabilitation services. RESULTS: The majority of training programs were at a hospital or subacute rehabilitation facility (Veteran Affairs and non-Veteran Affairs), and 41% of the programs were housed within an independent psychology department. Sixteen programs (15%) had faculty who were board certified by the American Board of Rehabilitation Psychology (ABRP). CONCLUSIONS/IMPLICATIONS: Interns were exposed to a broad range of conditions, such as brain injuries, orthopedic, and spinal cord injuries, as well as comorbid psychiatric and substance use disorders. Interns were also provided various levels of training in ABRP competencies across programs. Opportunities to improve training with rehabilitation populations at the internship level include increasing didactics related to rehabilitation psychology and increasing opportunities to work with ABRP faculty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".