Curriculum to enhance pharmacotherapeutic knowledge in family medicine
Bibliographic record
Abstract
Problem addressed Prescribing is an essential skill for physicians. Despite the fact that prescribing habits are still developing in residency, formal pharmacotherapy curricula are not commonplace in postgraduate programs. Objective of program To teach first-year and second-year family medicine residents a systematic prescribing process using a medication prescribing framework, which could be replicated and distributed. Program description A hybrid model of Web-based ( [www.rationalprescribing.com][1] ) and in-class seminar learning was used. Web-based modules, consisting of foundational pharmacotherapeutic content, were each followed by an in-class session, which involved applying content to case studies. A physician and a pharmacist were coteachers and they used simulated cases to enhance application of pharmacotherapeutic content and modeled interprofessional collaboration. Conclusion This systematic approach to prescribing was well received by family medicine residents. It might be important to introduce the process in the undergraduate curriculum—when learners are building their therapeutic foundational knowledge. Incorporating formal pharmacotherapeutic curriculum into residency teaching is challenging and requires further study to identify potential effects on prescribing habits. [1]: http://www.rationalprescribing.com
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".