Telehealth Curricula in the Pediatric Core Clerkship: Results From a Survey of Clerkship Directors
Bibliographic record
Abstract
OBJECTIVE: Given the increasing prevalence of telehealth, medical students require dedicated instruction in the practice of high-quality telehealth. This study characterizes telehealth practices and curricula in pediatric core clerkships across the United States and Canada. METHODS: We surveyed pediatric core clerkship directors and site directors through the 2020 Council on Medical Student Education in Pediatrics (COMSEP) annual member survey. We analyzed the results using descriptive statistics. RESULTS: Of 104 medical schools represented, 28 responded (26.9%). Directors reported students spent little time on telehealth during their pediatric core clerkships (average 8.2% of clerkship; SD 10.4). Only 10.7% (n=3) of clerkships had dedicated telehealth curricula. The instructional methods, content, and modes of evaluation varied across the clerkships' curricula. Barriers to implementation of telehealth curricula included lack of dedicated time in the existing curriculum (64.0%), lack of faculty time to teach (44.0%), lack of curricular materials (44.0%), students not participating in telehealth activities (40.0%) and lack of faculty expertise (36.0%). CONCLUSIONS: Most pediatric core clerkships do not include dedicated telehealth curricula, and the characteristics of existing curricula vary. Considering the rapid adoption of telemedicine, pediatric core clerkships merit additional support and guidance for the training of medical students in telehealth 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".