Compliance of Medical Students With Voluntary Use of Personal Data Assistants for Clerkship Assessments
Bibliographic record
Abstract
BACKGROUND: For several years, final-year students at McMaster University have been required to complete 10 mini-CEX type assessments per rotation. A similar system was being introduced at Ottawa. PURPOSE: To facilitate data capture, we decided to introduce a personal data assistant (PDA)-based system and evaluate its impact. METHOD: A randomized trial was designed to compare the acceptability of PDA and printed evaluation forms. The trial failed because of clerks' unwillingness to use PDAs. A focus group was held and user surveys were administered, chiefly by e-mail, to explore students' preference for printed forms. RESULTS: Thirty percent of invited clerks (52/176) agreed to use a PDA; 6% (11; 21% of those agreeing) recorded one or more encounters; 2% (4) recorded at least the minimum number of evaluations required by their program. Most survey respondents expressed concerns related primarily to the relative inconvenience of PDAs compared to paper, a judgment reflecting the time required both to install required software and to become familiar with the software and data entry form, and to record information via the form. A minority were also concerned about assessors' willingness or ability to use PDA forms. CONCLUSION: Before asking students and clinical supervisors to use a PDA-based encounter-evaluation form in clerkship, planners should conduct a careful assessment of the advantages and disadvantages for students of the system they hope to implement. The prima facie greater convenience and efficiency of the PDA may actually be offset by workplace disincentives and inefficiencies in data recording, relative to the incentives and efficiencies associated with a system based on printed (paper) forms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".