Bibliographic record
Abstract
The 2002 CMA Physician Resource Questionnaire has determined that personal computer use among Canadian physicians is nearing saturation coverage, with the proportion using them rising from 74% to 89% in the past 5 years. Although female physicians are still slightly less likely to use computers than their male counterparts (87% vs 90%), the difference is not statistically significant. More than 90% of physicians in younger age groups personally use computers, compared with 85% of those aged 55–64 and 68% of those aged 65 and older. GP/FPs are slightly less likely (86%) to use them personally than medical (93%) and surgical (91%) specialists. Thirty-eight percent of those not currently using computers indicated that they plan to do so in the next 12 months, while 44% had no plans to start. This year's PRQ also indicated that more than one-quarter (28%) of Canadian physicians currently use a personal digital assistant (PDA) in clinical practice, a 47% increase over 2001. Male physicians are somewhat more likely to use them in their practices than females (30% v. 24%). Those in the under-35 age group are by far the most likely (44%) to use them in practice, while those aged 65 and older are least likely (12%). GP/FPs are slightly less likely (25%) to use PDAs than medical (31%) and surgical (30%) specialists. — Shelley Martin, Senior Analyst, Research, Policy and Planning Directorate, CMA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.484 | 0.299 |
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".