Self-reported effects of computer workshops on physicians' computer use
Bibliographic record
Abstract
BACKGROUND: The need for physicians to be proficient in the use of computers is undeniable. As computers have become easier to use and more widespread, their use in medicine is expanding. Several organizations have produced continuing medical education programs to teach physicians about the use of computers in medicine but little has been reported on the effects of such programs. METHOD: We present the self-reported effects of a series of workshops that taught physicians about basic computer skills: information retrieval, the Internet, CD-ROMs, electronic mail, and computer-aided learning. RESULTS: A questionnaire mailed to 65 workshop participants yielded a response rate of 46% (n = 30). Of the 30 respondents, 27% (n = 8) had bought new hardware or software because of attending the workshops, with the most common purchase being a new computer. Fifty-seven percent (n = 17) had increased their use of computers, with the most common applications being use of the Internet for information retrieval and electronic mail.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".