Effects of educational outreach visits on prescribing of benzodiazepines and antipsychotic drugs to elderly patients in primary health care in southern Sweden
Bibliographic record
Abstract
BACKGROUND: Different methods have previously been tested to affect GPs' prescribing habits. Attention has been drawn to benzodiazepines and antipsychotic drugs that are associated with several adverse effects in the elderly. OBJECTIVE: To evaluate if educational outreach visits to GP practices can affect the prescribing of benzodiazepines and antipsychotic drugs to the elderly and to evaluate the opinions of the participating GPs on such education. METHODS: In the county of Skåne, Sweden, 41 GP practices were invited to participate in educational outreach visits. Fifteen GP practices accepted the invitation. Practices were randomised to active (8 practices, 23 physicians) and control group (7 practices, 31 physicians). After the educational outreach visits prescribing of benzodiazepines and antipsychotic drugs to patients 65 years or older were measured for 1 year. The control group participated in the education after the study period. The opinions of GPs on educational outreach visits were evaluated. RESULTS: One year after the educational outreach visits there were significant decreases in the active group compared to control group in the prescribing of medium- and long-acting benzodiazepines and total benzodiazepines but not so for antipsychotic drugs. CONCLUSIONS: Educational outreach visits can be effective in modifying GPs' prescribing habits. We have shown this to be so for prescribing of benzodiazepines to elderly patients in primary health care. Educational outreach visits are also very well appreciated by participating GPs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".