Teaching pharmacotherapeutics to family medicine residents
Bibliographic record
Abstract
PROBLEM BEING ADDRESSED Medication prescribing is becoming increasingly complex, and the need for formal curricula in pharmacotherapeutics and medication prescribing in accredited family medicine residency programs has been advocated. OBJECTIVE OF PROGRAM The main objective of the pharmacotherapeutic curriculum is to support the development of family medicine residents’ pharmacotherapeutic knowledge and medication prescribing skills required for rational prescribing. PROGRAM DESCRIPTION The curriculum has 4 main components: 1) a medication prescribing framework based on the main tasks and key decisions related to the prescribing of medications, 2) 12 pharmacotherapeutic topics identified in the needs assessment, 3) a 5-step process for session design used by the curriculum development team, and 4) a description of specific roles of facilitators involved in delivering the curriculum. Formative evaluation of the curriculum using resident focus groups has helped to inform the further development of its components. CONCLUSION A formalized curriculum was created to build knowledge of pharmacotherapeutics and effective medication prescribing skills, which are necessary for the current complex environment of patient care and medication management.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".