Needs assessment for enhancing pediatric clerkship readiness
Bibliographic record
Abstract
BACKGROUND: Many students report feeling inadequately prepared for their clinical experiences in pediatrics. There is striking variability on how pediatric clinical skills are taught in pre-clerkship curricula. METHODS: We asked students who completed their clerkships in pediatrics, family medicine, surgery, obstetrics-gynecology and internal medicine to rate their pre-clinical training in preparing them for each clerkship, specifically asking about medical knowledge, communication, and physical exam skills. Based on these results, we surveyed pediatric clerkship and clinical skills course directors at North American medical schools to describe the competence students should have in the pediatric physical exam prior to their pediatric clerkship. RESULTS: Close to 1/3 of students reported not feeling adequately prepared for their pediatrics, obstetrics-gynecology, or surgery clerkship. Students felt less prepared to perform pediatric physical exam skills compared to physical exam skills in all other clerkships. Pediatric clerkship directors and clinical skills course directors felt students should have knowledge of and some ability to perform a wide spectrum of physical exam skills on children. There were no differences between the two groups except that clinical skills educators identified a slightly higher expected competence for development assessment skills compared to pediatric clerkship directors. CONCLUSIONS: As medical schools undergo cycles of curricular reform, it may be beneficial to integrate more pre-clerkship exposure to pediatric topics and skills. Further exploration and collaboration establishing how and when to incorporate this learning could serve as a starting point for curricular improvements, with evaluation of effects on student experience and performance. A challenge is identifying infants and children for physical exam skills practice.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".