Development, Implementation and Evaluation of A Pain Management and Palliative Care Educational Seminar for Medical Students
Bibliographic record
Abstract
BACKGROUND: Despite calls for the development and evaluation of pain education programs during early medical student training, little research has been dedicated to this initiative. OBJECTIVES: To develop a pain management and palliative care seminar for medical students during their surgical clerkship and evaluate its impact on knowledge over time. METHODS: A multidisciplinary team of palliative care and pain experts worked collaboratively and developed the seminar over one year. Teaching methods included didactic and case-based instruction, as well as small and large group discussions. A total of 292 medical students attended a seminar during their third- or fourth-year surgical rotation. A 10-item test on knowledge regarding pain and palliative care topics was administered before the seminar, immediately following the seminar and up to one year following the seminar. Ninety-five percent (n=277) of students completed the post-test and 31% (n=90) completed the follow-up test. RESULTS: The mean pretest, post-test and one-year follow-up test scores were 51%, 75% and 73%, respectively. Mean test scores at post-test and follow-up were significantly higher than pretest scores (all P<0.001). No significant difference was observed in mean test scores between follow-up and post-test (P=0.559), indicating that students retained knowledge gained from the seminar. CONCLUSIONS: A high-quality educational seminar using interactive and case-based instruction can enhance students' knowledge of pain management and palliative care. These findings highlight the feasibility of developing and implementing pain education material for medical students during their training.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".