Implementation of a PDA based program to quantify urology resident in-training experience.
Bibliographic record
Abstract
INTRODUCTION: There currently is no simple and reliable mechanism for Residency program directors to assess how well their trainees are being exposed to all spheres of their specialty. We report on the use of hand-held personal digital assistants (PDA's) to document all clinical and academic activities of urology residents at one academic institution. MATERIALS AND METHODS: Software was developed to create customized pick lists allowing residents to record all activities on their individual PDA's. Categories included Adult Ambulatory, Pediatric Ambulatory, Adult operative, Pediatric operative, and Academic. Activities were subcategorized into detailed pick lists and time-tracking fields. Residents synchronized with a central database on a standalone hotsync server. RESULTS: In the first 8 months, 21 178 resident-hours and 5333 activities were recorded. Preliminary observations can be made regarding how residents spend the majority of their time: 28% operative, 20% self-study, 19% ward work, 10% Academics, 6% ER consultations, 5% clinic, and 4% inpatient consultations. The most common adult diagnoses encountered while attending to clinic, ward, or ER consultations were lower urinary tract symptoms, urolithiasis and hematuria. Similarly for Pediatrics: neurogenic bladder, antenatal hydronephrosis, infection, and hypospadias were most often reported. Residents reported 5,333 activities, relating to the following spheres of Urology: academics (23%), endourology (18%), oncology (15%), lower urinary tract symptoms (10%), congenital anomalies (5%), urolithiasis (5%), reconstruction (5%), and infection (3%). CONCLUSIONS: This tool provides an objective assessment of resident experience as it relates to selection of rotations, and for addressing curriculum weaknesses. It is applicable at a national level for the study of regional differences in training experience, and trends in graduate Urological education. With minimal effort it could be modified for application to other specialty training programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".