Bibliographic record
Abstract
INTRODUCTION: The literature shows that physicians do not change their patterns of practice quickly. With thousands of conflicting guidelines available, many disseminated by print, there has been little measurable impact of new guidelines on physician prescribing patterns. METHODS: A series of interventions designed to improve physician uptake of guidelines is described. Interventions evaluated in a series of trials include mail dissemination, small group continuing medical education sessions, academic detailing by both academic detailers and pharmaceutical detailers, and a whole community program known as the Program for Appropriate Anti-Infective Community Therapy. RESULTS: Only the whole community intervention had significant impact on physician prescribing patterns. The changes involved a significant reduction in anti-infective use and a significant change from the use of second- and third-line drugs to first-line drugs, as specified in the Ontario Anti-Infective Guidelines. CONCLUSIONS: Acceptable guidelines disseminated by a multidisciplinary community program can have a positive impact on physician prescribing patterns. To increase the acceptability of guidelines for whole community dissemination, a new innovation (Guideline Advisory Committee) that scores the quality of guidelines and selects the most objectively produced guideline is described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".