Where physicians look for information on drug prescribing for children
Bibliographic record
Abstract
BACKGROUND: Despite the important role of drug therapy in children, there is often a lack of readily available information regarding the indications and dosing regimens for medications in paediatrics. OBJECTIVE: To collect data on where family physicians obtain this prescribing information. METHOD: A structured questionnaire was mailed to 500 family physicians in Ontario. RESULTS: Questionnaires were returned by 261 (52%) family physicians, 217 (83%) of whom identified themselves as currently involved in the care of children. Most (87%) reported that the Compendium of Pharmaceuticals and Specialties (CPS) was the source that they most commonly consulted for drug information in children. The available sources of information on prescribing for children were thought to be not adequate by 40% and not readily available by 27%. Sixty-one per cent reported being moderately confident (in doubt part of the time) about their decisions regarding drug prescribing in this age group. The majority (70%) had learned most of what they know about prescribing in paediatrics during practice, while 69% and 62% reported they had little or no teaching during undergraduate and postgraduate (internship or residency) medical education, respectively. CONCLUSIONS: Although it is recognized that for a number of drugs used in children the CPS does not reflect the current standard of care in paediatrics, it is currently the source most commonly referred to by family physicians. Further work should be done in the provision of useful information on paediatric drug therapy to family physicians.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".