P217 The Effect Of Print Or Online Educational Materials For Primary Care Physicians: A Systematic Review
Bibliographic record
Abstract
Background Print and online materials such as guideline summaries are commonly used to distribute evidence to primary care physicians; they are easy to implement and scale across many primary clinics. Objectives We sought to determine: 1) if providing primary care physicians with print and online educational materials has an effect on physician behaviour or on patient outcomes, 2) how these materials were developed, and 3) whether design attributes impact outcomes. Methods We systematically identified studies that reported a print or online educational intervention for primary care physicians. Studies were identified by searching four electronic databases, scanning reference lists, and contacting experts. A sub-analysis was conducted to collect data on how these materials were developed and on their use of design principles. Results Thirty studies met eligibility criteria after full-text screening. Studies targeted physician advice-giving behaviour, diagnostic procedures, prescribing behaviour, change in knowledge, and clinical patient outcomes. Results suggest that print and online materials targeted at primary care physicians have little to no effect on outcomes. Discussion Print and online educational materials provided to primary care physicians have little effect on physician or patient outcomes. This is concerning as they are a common method of disseminating evidence. Most studies do not describe how interventional materials were developed or whether design principles were applied. Implications for Guideline Developers/Users Design principles should be considered when developing evidence-based materials and the development processes should be described in order to determine if better designs influence uptake and use of evidence.
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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 | 0.022 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".