Bibliographic record
Abstract
Results from the CMA's 2001 Physician Resource Questionnaire (PRQ) indicate that almost half of Canadian MDs (48%) refer patients to medical Web sites at least occasionally. There were no notable differences in referral rates by the sex, broad specialty or age of the physician, with the exception of those aged 65 and older — only 34% of respondents from that age group make these referrals. Almost all physicians who refer patients to Web sites (93%) are Internet users themselves. Among those who make these referrals, disease-specific sites received the most frequent recommendation (77%), followed by government (31%), consumer group (21%) and medical association (20%) sites. Only 8% referred patients to commercial health sites such as WellnessWeb. PRQ respondents were asked if they tend to recommend Canadian sites rather than American or other international sites. Among those who make these referrals, half prefer Canadian sites while 36% have no preference. GP/FPs were somewhat more likely to prefer to send patients to Canadian sites (57%) than were medical specialists (43%) or surgical specialists (39%). Among those who tend to recommend Canadian sites, 89% do so because they prefer sites oriented to Canada's health care system, while 28% prefer those that offer information either in French or in both official languages. Respondents without a preference for Canadian sites said that the content of American/international sites is appropriate for Canadian patients (52%), that equivalent Canadian sites do not exist (31%) or that American/ international sites are of higher quality and/or are more comprehensive than their Canadian counterparts (27%). Surgical specialists were somewhat more likely to state that non-Canadian sites are higher quality or more comprehensive (40%) than were medical specialists (32%) or GP/FPs (17%). Full results from the 2001 PRQ are available at www.cmaj .ca /cgi /content /full/165 /5 /626 / DC1. — Shelley Martin, Senior Analyst, Research, Policy and Planning Directorate, CMA
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.800 | 0.667 |
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".