Use and perceptions of information among family physicians: sources considered accessible, relevant, and reliable
Bibliographic record
Abstract
OBJECTIVES: The research determined (1) the information sources that family physicians (FPs) most commonly use to update their general medical knowledge and to make specific clinical decisions, and (2) the information sources FPs found to be most physically accessible, intellectually accessible (easy to understand), reliable (trustworthy), and relevant to their needs. METHODS: A cross-sectional postal survey of 792 FPs and locum tenens, in full-time or part-time medical practice, currently practicing or on leave of absence in the Canadian province of Saskatchewan was conducted during the period of January to April 2008. RESULTS: Of 666 eligible physicians, 331 completed and returned surveys, resulting in a response rate of 49.7% (331/666). Medical textbooks and colleagues in the main patient care setting were the top 2 sources for the purpose of making specific clinical decisions. Medical textbooks were most frequently considered by FPs to be reliable (trustworthy), and colleagues in the main patient care setting were most physically accessible (easy to access). CONCLUSIONS: When making specific clinical decisions, FPs were most likely to use information from sources that they considered to be reliable and generally physically accessible, suggesting that FPs can best be supported by facilitating easy and convenient access to high-quality information.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".