Effectiveness of Academic Detailing to Optimize Medication Prescribing Behaviour of Family Physicians
Bibliographic record
Abstract
PURPOSE: To synthesize current knowledge about the effectiveness and the magnitude of the effect, of Academic Detailing (AD), as a stand-alone intervention, at modifying drug prescription behavior of Family Physicians (FPs) in primary care settings. METHODS: A search of MEDLINE, EMBASE, CENTRAL, and Web of Science databases of all English language articles between January 1983 and July 2010 was conducted. We hand-searched the bibliographies of articles retrieved from the electronic search to identify additional studies. Inclusion criteria were: full-length articles describing original research; randomized controlled trial (RCT), or observational study design with a control group; studies of AD delivered to FPs; AD as a stand-alone intervention; drug prescription as the target behavior. Data extraction was done independently by two reviewers. Outcomes evaluated were: the difference in relative change in prescription rate between the intervention and control groups; the difference in absolute change in prescription rate between the intervention and control groups; and effect size, calculated as the standardized mean difference. RESULTS: 11 RCTs and 4 observational studies were included. Five RCTS described results showing effectiveness, while 2 RCTs reported a positive effect on some of the target drugs. Two observational studies found AD to be effective, while 2 did not. The median difference in relative change among the studies reviewed was 21% (interquartile range 43.75%) for RCTs, and 9% (interquartile range 8.5%) for observational studies. The median effect size among the studies reviewed was - 0.09 (interquartile range 2.73). CONCLUSION: This systematic review demonstrates that AD can be effective at optimizing prescription of medications by FPs. Although variable, the magnitude of the effect is moderate in the majority of studies. This systematic review also provides evidence supportive of the use of AD as a strategy to promote evidence based prescription of medications or incorporation of clinical guidelines into clinical practice. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".