Better Prescribing Project: a randomized controlled trial of the impact of case-based educational modules and personal prescribing feedback on prescribing for hypertension in primary care
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess the impacts of individualized prescribing feedback and interactive small group education aimed at encouraging evidence-based prescribing in family/general practice. METHODS: A two-by-two factorial randomized controlled trial was carried out involving 200 family physicians in British Columbia, Canada. The physicians met monthly in 28 peer learning groups within the Practice-Based Small Group (PBSG) learning programme. Personalized prescribing feedback related to hypertension was provided through 'prescribing portraits' which graphically displayed comparative rates of individual and peer group prescribing, together with a concise guide for evidence-based prescribing. A case-based educational module, containing the same evidence-based message, was discussed in small groups. Groups were matched and randomized into four arms of seven groups each: control (n = 56), prescribing portrait only (n = 48), educational module only (n = 47), both portrait and module (n = 49). The main outcome measure was changes in 'prescribing preferences' to new patients among those medications used to treat hypertension (i.e. probability that a patient would receive the evidence-based medication as first-line therapy). RESULTS: Using data from the provincial pharmacy registry (PharmaNet), prescribing preferences for antihypertensive agents were determined for all groups for a 6 month period before and after the interventions, based on 4394 patients receiving a first-line antihypertensive. Significant absolute increases in prescribing preference for thiazides were documented for both the module +0.068 (confidence interval [CI] 0.022-0.115) and the portrait +0.065 (CI 0.018-0.111). Preference in the group receiving both module and portrait increased by +0.115 (CI 0.040-0.189). CONCLUSIONS: Evidence-based educational interventions combining personalized prescribing feedback with interactive group discussion can lead to modest but meaningful changes in physician prescribing. Clear messages, proper trial design and sensitive outcomes are necessary to demonstrate these changes.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".