Searching for medical information online: a survey of Canadian nephrologists
Bibliographic record
Abstract
BACKGROUND: Physicians often search for information to improve patient care. We evaluated how nephrologists use online information sources for this purpose. METHODS: In this cross-sectional study (2008 to 2010), a random sample of Canadian nephrologists completed a survey of their online search practices. We queried respondents on their searching preferences, practices and use of 9 online information sources. RESULTS: Respondents (n=115; 75% response rate) comprised both academic (59%) and community-based (41%) nephrologists. Respondents were an average of 48 years old and were in practice for an average of 15 years. Nephrologists used a variety of online sources to retrieve information on patient treatment including UpToDate (92%), PubMed (89%), Google (76%) and Ovid MEDLINE (55%). Community-based nephrologists were more likely to consult UpToDate first (91%), while academic nephrologists were divided between UpToDate (58%) and PubMed (41%). When searching bibliographic resources such as PubMed, 80% of nephrologists scan a maximum of 40 citations (the equivalent of 2 search pages in PubMed). Searching practices did not differ by age, sex or years in practice. CONCLUSIONS: Nephrologists routinely use a variety of online resources to search for information for patient care. These include bibliographic databases, general search engines and specialized medical resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".