The Impact of a Personal Digital Assistant (PDA) Case Log in a Medical Student Clerkship
Bibliographic record
Abstract
BACKGROUND: Medical education literature emphasizes that reflection and self-audit are pivotal steps in learning and that personal digital assistants (PDAs) have potential as decision support tools. DESCRIPTION: The purpose was to examine the efficacy of PDA-based resources and patient-encounter logging systems among 3rd-year medical clerks during pediatrics rotations. EVALUATION: Students in rotations were assigned to control (using paper-based logs and references) or intervention groups (using PDA-based logs and resources). Students completed pre- and postrotation Paediatrics Competency Surveys, participated in focus groups, and were compared on year-end examination grades. Use of PDA logs far outweighed that of paper logs (1,020 PDA logs and 87 paper logs). PDA logs were ranked significantly higher in enhancing learning and reflection than paper logs (t = 2.52, p < .01). PDA logs also facilitated specific learning experiences. CONCLUSION: PDA-based patient-encounter logs appear to be effective case documentation and reflection tools. The difference in number of logs between control and intervention groups demonstrates the utility of the PDA for "point-of-care" patient logging.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".