Multi-institutional Development and Utilization of a Computer-Assisted Learning Program for the Pediatrics Clerkship: The CLIPP Project
Bibliographic record
Abstract
Computer-assisted instruction (CAI) holds significant promise for meeting the current challenges of medical education by providing consistent and quality teaching materials regardless of training site. The Computer-assisted Learning in Pediatrics Project (CLIPP) was created over three years (2000-2003) to meet this potential through multi-institutional development of interactive Internet-based patient simulations that comprehensively teach the North American core pediatrics clerkship curriculum. Project development adhered to four objectives: (1) comprehensive coverage of the core curriculum; (2) uniform approach to CAI pedagogy; (3) multi-institutional development by educators; and (4) extensive evaluation by users. Pediatrics clerkship directors from 30 institutions worked in teams to develop a series of 31 patient case simulations. An iterative process of case content and pedagogy development, case authoring, peer review, and pilot-testing ensured that the needs of clerkship directors and medical students were met. Fifty medical schools in the United States and Canada are presently using CLIPP. More than 8,000 students have completed over 98,000 case sessions, with an average of 2,000 case sessions completed per week at this time. Each CLIPP case has been completed by more than 3,000 students. The current cost of CLIPP development is approximately $70 per student user, or $6 per case session. The project's success demonstrates that multi-institutional development and implementation of a peer-reviewed comprehensive CAI learning program by medical educators is feasible and provides a useful model for other organizations to develop similar programs. Although CAI development is both time-consuming and costly, the initial investment decreases significantly with broad use over time.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".