metadat (version 1.4-0)

dat.baskerville2012: Studies on the Effectiveness of Practice Facilitation Interventions

Description

Results from 23 studies on the effectiveness of practice facilitation interventions within the primary care practice setting.

Usage

dat.baskerville2012

Format

The data frame contains the following columns:

author	character	study author(s)
year	numeric	publication year
score	numeric	quality score (0 to 12 scale)
design	character	study design (cct = controlled clinical trial, rct = randomized clinical trial, crct = cluster randomized clinical trial)
alloconc	numeric	allocation concealed (0 = no, 1 = yes)
blind	numeric	single- or double-blind study (0 = no, 1 = yes)
itt	numeric	intention to treat analysis (0 = no, 1 = yes)
fumonths	numeric	follow-up months
retention	numeric	retention (in percent)
country	character	country where study was conducted
outcomes	numeric	number of outcomes assessed
duration	numeric	duration of intervention
pperf	numeric	practices per facilitator
meetings	numeric	(average) number of meetings
hours	numeric	(average) hours per meeting
tailor	numeric	intervention tailored to the context and needs of the practice (0 = no, 1 = yes)
smd	numeric	standardized mean difference
se	numeric	corresponding standard error

Author

Wolfgang Viechtbauer, , https://www.metafor-project.org

Concepts

medicine, primary care, standardized mean differences, publication bias, meta-regression

Details

Baskerville et al. (2012) describe outreach or practice facilitation as a "multifaceted approach that involves skilled individuals who enable others, through a range of intervention components and approaches, to address the challenges in implementing evidence-based care guidelines within the primary care setting". The studies included in this dataset examined the effectiveness of practice facilitation interventions for improving some relevant evidence-based practice behavior. The effect was quantified in terms of a standardized mean difference, comparing the change (from pre- to post-intervention) in the intervention versus the comparison group (or the difference from baseline in prospective cohort studies).

Examples

### copy data into 'dat' and examine data
dat <- dat.baskerville2012
dat

if (FALSE) {
### load metafor package
library(metafor)

### random-effects model
res <- rma(smd, sei=se, data=dat, method="DL")
print(res, digits=2)

### funnel plot
funnel(res, xlab="Standardized Mean Difference", ylim=c(0,0.6))

### rank and regression tests for funnel plot asymmetry
ranktest(res)
regtest(res)

### meta-regression analyses examining various potential moderators
rma(smd, sei=se, mods = ~ score, data=dat, method="DL")
rma(smd, sei=se, mods = ~ alloconc, data=dat, method="DL")
rma(smd, sei=se, mods = ~ blind,    data=dat, method="DL")
rma(smd, sei=se, mods = ~ itt,      data=dat, method="DL")
rma(smd, sei=se, mods = ~ duration, data=dat, method="DL")
rma(smd, sei=se, mods = ~ tailor,   data=dat, method="DL")
rma(smd, sei=se, mods = ~ pperf,    data=dat, method="DL")
rma(smd, sei=se, mods = ~ I(meetings * hours), data=dat, method="DL")
}