What is the effectiveness of printed educational materials on primary care physician knowledge, behaviour, and patient outcomes: a systematic review and meta-analyses
Bibliographic record
Abstract
BACKGROUND: Printed educational materials (PEMs) are commonly used simple interventions that can be used alone or with other interventions to disseminate clinical evidence. They have been shown to have a small effect on health professional behaviour. However, we do not know whether they are effective in primary care. We investigated whether PEMs improve primary care physician (PCP) knowledge, behaviour, and patient outcomes. METHODS: We conducted a systematic review of PEMs developed for PCPs. Electronic databases were searched for randomized controlled trials, quasi randomized controlled trials, controlled before and after studies, and interrupted time series. We combined studies using meta-analyses when possible. Statistical heterogeneity was examined, and meta-analysis was performed using a random effects model when significant statistical heterogeneity was present and a fixed effects model otherwise. The template for intervention description and replication (TIDieR) checklist was used to assess the quality of intervention description. RESULTS: Our search identified 12,439 studies and 40 studies met our inclusion criteria. We combined outcomes from 26 studies in eight meta-analyses. No significant effect was found on clinically important patient outcomes, physician behaviour, or physician cognition when PEMs were compared to usual care. In the 14 studies that could not be included in the meta-analyses, 14 of 71 outcomes were significantly improved following receipt of PEMs compared to usual care. Most studies lacked details needed to replicate the intervention. CONCLUSIONS: PEMs were not effective at improving patient outcomes, knowledge, or behaviour of PCPs. Further trials should explore ways to optimize the intervention and provide detailed information on the design of the materials. PROTOCOL REGISTRATION: PROSPERO, CRD42013004356.
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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 | 0.033 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.031 | 0.064 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".