Are personal digital assistants an acceptable incentive for rural community-based preceptors?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This study's objective was to evaluate the acceptability, effect, and use of handheld computers (also known as personal digital assistants or PDAs) as a reward for undergraduate rural community-based family medicine preceptors. METHODS: All rural, undergraduate family physician teachers who accepted an undergraduate student for a 1-month placement were offered the choice between a PDA that carried medical software or a monetary payment of an equivalent value. Approximately 1 year later, different surveys were sent to both groups of preceptors to collect data on their use of PDAs and computer technology. RESULTS: The most commonly reported reason for choosing a PDA in lieu of payment was that it provided a good opportunity to learn about PDA technology. Of those who accepted a PDA, however, 10% had not yet used it, and another 44% of recipients had difficulty in getting started using the PDA. There were more reported problems with the software than the hardware. When surveyed 1 year later, those who received a PDA and were still using it reported satisfaction with the medical software, ranging from 31% for Epocrates qid to 71% for the 5-Minute Medical Consult. More than 90% of those using their PDA 1 year later reported that they used it in clinical settings, with 68% feeling their PDA had some or a significant effect on patient care. CONCLUSIONS: Rural family physicians appeared to find PDAs an acceptable reward for teaching, based on the reported use and utility of their PDA, but many had technical difficulties. Recipients of the PDA reported using their PDA primarily in the clinical setting, with the feeling that the PDA had a positive effect on their patient care. Many users had difficulty with technical aspects of PDA use. To support PDA recipients, technical assistance should be provided.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".