Learning the “Science of the Art of Prescribing”: From Evidence-based Algorithms to Individualized Medicine in Psychiatric Care
Bibliographic record
Abstract
The purpose of this review is to highlight the limitations of the traditional diagnosis/evidence-based symptom reduction paradigm and advocate for an individualized medicine approach that incorporates psychological and relational aspects of prescribing in addition to the objective patient presentation. Potential barriers, challenges, and proposed future directions for improving education in psychological and relational aspects of prescribing are discussed. Psychological aspects of prescribing, as recently spelled out in the field of psychodynamic psychopharmacology, are generally acknowledged as important, but they do not have a well-defined position in contemporary residency training throughout North America. While residents receive in-depth exposure to diverse aspects of what to prescribe in their psychopharmacological training, and they work with patients' subjective and relational meaning and the quality of the therapeutic alliance in their psychotherapy rotations, an integrated approach to how to prescribe is generally lacking. Despite many legitimate challenges, the authors suggest that teaching an integrated approach that incorporates objective, subjective, and relational factors in the provision of psychopharmacology and utilizing evidence-based principles of individualized care should be prioritized in both residency training and the provision of psychiatric treatment as a whole.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".