Psychologists in medical schools and academic medical centers: Over 100 years of growth, influence, and partnership.
Bibliographic record
Abstract
Psychologists have served on the faculties of medical schools for over 100 years. Psychologists serve in a number of different roles and make substantive contributions to medical schools' tripartite mission of research, education, and clinical service. This article provides an overview of the history of psychologists' involvement in medical schools, including their growing presence in and integration with diverse departments over time. We also report findings from a survey of medical school psychologists that explored their efforts in nonclinical areas (i.e., research, education, administration) as well as clinical endeavors (i.e., assessment, psychotherapy, consultation). As understanding of the linkage between behavioral and psychological factors and health status and treatment outcomes increases, the roles of psychologists in health care are likely to expand beyond mental health. An increasing focus on accountability-related to treatment outcomes and interprofessional research, education, and models of care delivery-will likely provide additional opportunities for psychologists within health care and professional education. The well-established alignment of psychologists' expertise and skills with the mission and complex organizational needs of medical schools augurs a partnership on course to grow stronger. (PsycINFO Database Record (c) 2014 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".