Systematic Review and Meta-Analysis of Practice Facilitation Within Primary Care Settings
Bibliographic record
Abstract
PURPOSE: This study was a systematic review with a quantitative synthesis of the literature examining the overall effect size of practice facilitation and possible moderating factors. The primary outcome was the change in evidence-based practice behavior calculated as a standardized mean difference. METHODS: In this systematic review, we searched 4 electronic databases and the reference lists of published literature reviews to find practice facilitation studies that identified evidence-based guideline implementation within primary care practices as the outcome. We included randomized and nonrandomized controlled trials and prospective cohort studies published from 1966 to December 2010 in English language only peer-reviewed journals. Reviews of each study were conducted and assessed for quality; data were abstracted, and standardized mean difference estimates and 95% confidence intervals (CIs) were calculated using a random-effects model. Publication bias, influence, subgroup, and meta-regression analyses were also conducted. RESULTS: Twenty-three studies contributed to the analysis for a total of 1,398 participating practices: 697 practice facilitation intervention and 701 control group practices. The degree of variability between studies was consistent with what would be expected to occur by chance alone (I2 = 20%). An overall effect size of 0.56 (95% CI, 0.43-0.68) favored practice facilitation (z = 8.76; P <.001), and publication bias was evident. Primary care practices are 2.76 (95% CI, 2.18-3.43) times more likely to adopt evidence-based guidelines through practice facilitation. Meta-regression analysis indicated that tailoring (P = .05), the intensity of the intervention (P = .03), and the number of intervention practices per facilitator (P = .004) modified evidence-based guideline adoption. CONCLUSION: Practice facilitation has a moderately robust effect on evidence-based guideline adoption within primary care. Implementation fidelity factors, such as tailoring, the number of practices per facilitator, and the intensity of the intervention, have important resource implications.
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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 | 0.053 | 0.161 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.042 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".