Continuing educational needs in computers and informatics. McGill survey of family physicians.
Bibliographic record
Abstract
OBJECTIVE: To describe family physicians' perceived educational needs in computers and informatics. DESIGN: Mailed survey. SETTING: General or family practices in Canada. PARTICIPANTS: Physicians (489 responded to a mailing sent to 2,500 physicians) who might attend sessions at the McGill Centre for CME. Two duplicate questionnaires were excluded from the analysis. METHOD: Four domains were addressed: practice profile, clinical CME needs, professional CME needs, and preferred learning formats. Data were entered on dBASE IV; analyses were performed on SPSS. MAIN FINDINGS: In the 487 questionnaires retained for analysis, "informatics and computers" was mentioned more than any other clinical diagnostic area, any other professional area, and all but three patient groups and service areas as a topic where improvement in knowledge and skills was needed in the coming year. Most physicians had no access to computer support for practice (62.6%); physicians caring for neonates, toddlers, or hospital inpatients were more likely to report some type of computer support. CONCLUSIONS: Family physicians selected knowledge and skills for computers and informatics as an area for improvement in the coming year more frequently than they selected most traditional clinical CME topics. This educational need is particularly great in small towns and in settings where some computerized hospital data are already available.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".