A novel clinical pharmacy experience for third-year medical students.
Bibliographic record
Abstract
A novel, clinical curriculum was developed to teach third-year medical students the principles of prescribing for elderly people. The experience involved a didactic session with a community pharmacist and a home visit to assess a senior citizen volunteer who was over age 75 years and was prescribed more than five medications. The medical students completed pre- and postexperience questionnaires to assess knowledge and opinions. Statistical analysis used paired t tests to compare pre- and postknowledge. The percentage agreeing or disagreeing were calculated for Likert opinion responses by using mean summary scores. Pre- and postexperience results were compared using paired t tests. Students showed improved knowledge scores on recognizing drug-drug (P=0.029) and drug-disease interactions (P=0.012). Knowledge on true/false prescribing questions was improved (P=0.005). Students felt that their current curriculum gave insufficient time to prescribing issues, and wanted more education about the use of medications and appropriate prescribing. The majority of students felt that they learned new things (81%), the experience was enjoyable (65%), important topics were covered (71.4%) and they would be more likely to confer with a community pharmacist because of the experience (75%). The novel curriculum described appears to be effective and warrants further evaluation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".