Bibliographic record
Abstract
There is little doubt that undergraduate and post-graduate training of physicians, pharmacists, and nurses is insufficient to prepare them to use psychotropics safely and effectively, especially in the context of their expanded off-label uses. Therefore, the development of competencies in psychotropic prescribing needs to be approached as a long-term, practice-based learning commitment. Proposed are the abilities and knowledge components necessary for safe and effective use of psychotropics. Typical challenges in prescribing for chronic and recurrent illnesses include highly variable responses and tolerability, drug interactions, and adverse effects that can be serious, irreversible, and even fatal. Prescribing psychotropics is further complicated by negative public and professional reports and growing patient concerns about the quality of care, and questions about the efficacy, safety, and addictive risks of psychotropics. Increased efforts are needed to enhance clinical training and knowledge in psychopharmacology among trainees and practising clinicians, with more comprehensive and sustained attention to the assessment of individual patients, and greater reliance on patient education and collaboration. Improved competence in psychotropic prescribing should lead to more informed, thoughtful, and better-targeted applications as one component of more comprehensive clinical care.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".