Measuring Prescribing Improvements in Pragmatic Trials of Educational Tools for General Practitioners
Bibliographic record
Abstract
Randomized pragmatic trials of drugs, physician education and drug policies are needed to improve pharmacosurveillance and cost-effectiveness of prescribing. Since 1994, we have developed and tested methods for low-cost education and policy trials to improve prescribing in primary care in Canada. We review methodology for using drug claims and other health services data to evaluate prescribing improvement programs and policies. We apply the lessons to a proposed trial of physician education tools (PET) for quality improvement of prescribing. Design issues for the trial include defining the potential programme in causal terms using counterfactuals, narrowing the denominator to the population affected, excluding noise from the numerator, calculating the prescribing preference, adjusting for baseline differences, controlling for modifiers and confounders, accounting for uncertainty when measuring impacts, and grouping practices for feedback and recognition. Data from a randomized trial of academic detailing illustrate measurement challenges. A decade of progress on methods for evaluating prescribing improvement programs with drug claims data has enabled planning of routine randomized pragmatic trials of education and policies in primary care in Canada.
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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 | 0.059 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".