Toward a new predoctoral model: Education and training in clinical psychopharmacology.
Bibliographic record
Abstract
A ubiquitous research finding in regional and national studies is that at least 40% of persons with mental disorders cannot access mental health services, and pharmacotherapy in particular. The American Psychological Association's (APA) designated programs for the provision of education and training in clinical psychopharmacology can be of great help in alleviating this national need. We address key developments relevant to the foundation of a predoctoral model of clinical psychopharmacology education and training. To this end, an overview of the Master of Science in Clinical Psychopharmacology (MSCP) program at The Chicago School of Professional Psychology (TCSPP) is presented. TCSPP is now enrolling its eleventh consecutive cohort of MSCP students, many of whom are doctoral students who are concurrently attending various APA accredited Health Service Psychology (HSP) programs. We provide two predoctoral routes for completing MSCP training: (a) a route allowing for the creation of concentrations in clinical psychopharmacology in Health Service Psychology (HSP) doctoral programs, providing up to half of MSCP coursework; and (b) a joint doctoral PsyD or PhD/MSCP program meeting APA accreditation and designation standards integrated into a 5-year curriculum to impart HSP graduates with the competencies to provide both psychotherapy and pharmacotherapy. We conclude with a discussion about the future direction of predoctoral clinical psychopharmacology education and training. Given its emphasis on neuroscience and interdisciplinary health care, such curricular models may help to address the nation's immediate mental health care needs, while serving to enhance the sustainability of HSP education and professional practice in the 21st Century. (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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".