Improving on-line skills and knowledge. A randomized trial of teaching rural physicians to use on-line medical information.
Bibliographic record
Abstract
OBJECTIVE: To assess the change in frequency and methods with which a pilot group of rural physicians consulted on-line medical resources before and after an educational intervention. DESIGN: Physicians were randomly assigned to an educational intervention or control group. Self-administered questionnaires were completed before and 3 months after the intervention. SETTING: Rural practices in southwestern Ontario. PARTICIPANTS: Eighty rural (defined as a population of 15000 or less) physicians in southwestern Ontario, with a computer with Internet access. INTERVENTIONS: Individualized 3-hour training session on using the World Wide Web to research patient-related questions. MAIN OUTCOME MEASURES: Frequency of access and comfort with on-line medical information were compared after intervention with baseline data using the Wilcoxon two-sample test. RESULTS: At follow up, the intervention group showed a significant improvement over the control group in their frequency of accessing the World Wide Web to address patient-related questions (P = .009), in their comfort level in using on-line databases (P = .032), and in their frequency of accessing on-line databases (P = .044). CONCLUSION: Rural physicians' comfort and competence in using computers to address patient problems can be improved by an individualized 3-hour training session.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".