Student perceptions of the care of children: impacts of pre-clerkship pediatric and primary care clinical teaching
Bibliographic record
Abstract
BACKGROUND: Pediatric clinical skills teaching sessions provide an early opportunity for students to be exposed to the medical care of children. This report describes second and third year medical students' perceptions of and attitudes towards working with children before and after the pediatric clinical skills teaching sessions, and the experiences of those students precepted by pediatricians only compared to those students working with a combination of pediatricians and family physicians. METHOD: A 13 question survey was voluntarily completed before and after teaching sessions. Written reflective assignments were qualitatively analyzed for key themes. Response rate averaged 68% with class sizes of 84 and 85 students. RESULTS: Students' perceptions of the care of children were generally very positive. Some differences were found based on gender, phase of study and prior clinical exposure to pediatric care. Pre and post responses were similar, regardless of preceptor specialty. Students with family physician preceptors identified the themes of prevention, health promotion and multidisciplinary care in their reflections. CONCLUSIONS: Students had already formed positive attitudes toward the medical care of children and intended to care for children in their future practice. Further research is needed into the effects of pre-clerkship experiences in the care of children on choice of medical specialty.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".