The patient safety strategies included in the present study were adopted from earlier Dutch research (1), examining the variables affecting patient safety management (PSM): facilities in the practice, communication and collaboration, , education regarding patient safety. Therefore, it is assumed that (Figure 1):
H1: Common perception generic conditions for patient safety are antecedents of: (a) facilities in the practice, (b) communication and collaboration and (c) education on patient safety).
H2: The three strategies (a) facilities in the practice, (b) communication and collaboration and (c) education on patient safety are antecedents of patient safety management.
The population used in this study was varied (table 1). We check whether the control variables have no influence on the measured effect (table 5) (15), it is assumed that :
H3: All control variables have identical relationships with strategies .
The model was analyzed using structural equation modeling (SEM): an extension of linear regression that enables the modeling of complex relationships between interrelated (and thus correlated) variables. The resulting models account for direct and indirect impacts and can cope with correlated variables. It can also be used to model causal relationships. In SEM, it is possible to use more than one attribute to characterize latent variables. SEM uses an optimization process to create an index score for each latent variable using linear regression.
In this study, SEM was used to characterize latent variables and to produce a causal model incorporating several of them. The models allow the diagnosis and assessment of potential causes of safety management failure, as well as the identification of relevant management methods.
3.1 Participants
Participants were recruited from several conferences and seminars in Poland where the strategies were presented; most of respondents were physicians potentially interested in patient safety. Further contacts were recruited through a snowball sampling procedure. In order to obtain the reliability of the answers, context descriptions were provided. Participation in the study was voluntary. A professional research and consulting company collected the questionnaires from the Polish respondents. During March 2019, 251 replies were received from1300 targeted respondents. Out of these 251 replies, three were not completed and were not included in the analysis. Therefore, the questionnaire was completed by 248 individuals (Table 1).
The obtained data was monitored manually. If the questionnaire was not completed correctly, or not at all, the respondent was contacted. We confirmed our understanding of the individual responses.
The respondents differed significantly with regard to the number of patients cared for one physician (ranging from 0 to 4,000; mean 1.763 patients; SD = 873) and the size of population of their counseling centers (ranging from 300 to 16,220 patients; mean 5091; SD =3.096). In addition, 49.6% of respondents conducted their medical practice in a city with over 100,000 people.
Table 1. Sample profile (N = 248)
Characteristics
|
n
|
%
|
Gender
|
|
|
|
Male
|
132
|
53.2
|
|
Female
|
116
|
46.8
|
Age [value from 29 to 63], mean and standard deviation
|
43
|
7
|
Current professional discipline
|
|
|
|
GP (general practitioner)
|
141
|
56.9
|
|
Internist
|
102
|
41.1
|
|
Other primary care physician
|
51
|
20.6
|
|
Medical teacher
|
10
|
4
|
|
Scientific researcher
|
10
|
4
|
|
Other
|
6
|
2.4
|
Current professional discipline *
|
|
|
|
Individual
|
61
|
24.6
|
|
Group
|
53
|
21.4
|
|
counseling centers
|
164
|
66.1
|
Number of patients per respondent [value from 0 to 4,000], mean (standard deviation)
|
1,763
|
872.96
|
Number of patients in the facility [value from 300 to 16,220] , mean and standard deviation
|
5,091
|
3,095.5
|
Area of practice
|
|
|
|
city with over 100,000 inhabitants
|
123
|
49.6
|
|
city of 30,000 to 100,000 inhabitants
|
37
|
14.9
|
|
city with less than 30,000 inhabitants
|
44
|
17.7
|
|
small town / village
|
44
|
17.7
|
The validity of the questionnaire was not evaluated because it had been adapted from a previous study (1). The questionnaire was designed to identify factors influencing patient safety in primary care. It comprised 38 items which were adopted from multiple scales used elsewhere in the literature. The instrument comprises the following five constructs that were analyzed by SEM: patient safety management, facilities in the practice, generic conditions, communication and collaboration, and education. A pilot test with 10 participants was run to remove irrelevant or weak questions. The collected data was carefully cleaned before analysis. Principal Component Analysis (PCA) was used to identify the primary components that were measured with the survey questions. Internal consistency was checked with a standard Cronbach’s Alpha test.
3.2 Measurement of latent variables
The instrument used in the study consists of 38 items measuring the five latent variables (strategies). All measures are scored on a four-point Likert scale providing sufficient variance and covariance for better data analysis (18). In addition, all the manifest variables included in the instrument, i.e. the items, reflect the changes of their corresponding latent variables and therefore can be seen as being caused by constructs (19). In addition, all the constructs are operationalized as first-order latent variables to reduce the complexity of the whole model, as the number of latent variables did not increase. In addition, as no blocks of indicators were found to share specific common characteristics, all items were treated as a single latent variable. A more detailed specification of items is presented in appendix Table A1.
Unrotated principal component factor analysis (CFA), principal component analysis with varimax rotation, and principal axis analysis with varimax rotation all revealed the presence of three distinct factors with eigenvalue greater than 1.0, rather than a single factor (20). The seven factors together accounted for 67.84 percent of the total variance; the first (largest) factor did not account for a majority of the variance (11.324%). While the results of these analyses do not preclude the possibility of common method variance, they do suggest that common method variance is not of great concern and thus is unlikely to confound the interpretations of results.
After 248 observations, the accuracy of each of the hidden variables was found to be at least 0.7, as measured by Cronbach’s alpha: each variable also demonstrated composite reliability (CR) of >0.7 and average variance extracted (AVE) of >0.5. The latent variable was not constructed from all of the observable variables proposed by (1). The strength of these considerations deliberations is to meet the threshold conditions by the constructed variables and the observable variables representing them.
Table 2. Discriminant validity for constructs and their correlations
|
Mean
|
standard deviation
|
R2
|
FP
|
CC
|
PSM
|
GC
|
EPS
|
FP
|
1.493
|
.591
|
.356
|
.711
|
|
|
|
|
CC
|
1.221
|
.522
|
.412
|
.488
|
.927
|
|
|
|
PSM
|
1.529
|
.653
|
.365
|
.543
|
.688
|
.710
|
|
|
GC
|
1.245
|
.466
|
.356
|
.503
|
.590
|
.555
|
.709
|
|
EPS
|
1.077
|
.365
|
.593
|
.408
|
.486
|
.615
|
.627
|
.710
|
Note: CC: communication and collaboration, EPS: education on patient safety, FP: facilities in the practice, GC: generic conditions, PSM: patient safety management, R2 : coefficient of determination,
the square root value of AVE is shown on the diagonal, under the diagonal of the Pearson correlation coefficient. For all p <0.001.
The square root of the AVEs are compared with the appropriate correlation factors in Table 2. They have much higher values, indicating positive divergent validity, i.e. the individual latent variables differ significantly from one another. In addition, discriminant validity analysis was performed to determine whether the measures of each construct differ sufficiently from those of other constructs (17).
The part of the model that examines relationship between the latent variables and their measures is known as the measurement model. A previous CFA based on a sample of respondents in Poland confirmed that the measurement model demonstrates satisfactory construct validity, discriminant validity and internal consistency (21,22). In the case of the measurement models, there is no reason to reject the hypothesis that the standardized residual values of the empirical and theoretical matrix are equal to zero (χ2 = 564.812; p = 0.000). The model was found to demonstrate a good fit to the data, as indicated by a root mean square of approximation error (RMSEA) of 0.061<0.08 (LO=0.054; HI=0.069), and to demonstrate good acceptability, as indicated by χ2/ss=1.928<2, GFI=0.951>0.9 and AGFI=0.921>0.9 (16,21,23). All latent variables in the model are significantly correlated, as shown in Table 3.
Table 3. Covariance and correlation between latent variables
Relation
|
Covariances
|
S.E.
|
C.R.
|
Correlations
|
EPS
|
<-->
|
CC
|
.127
|
.029
|
4.428
|
.486***
|
EPS
|
<-->
|
PSM
|
.119
|
.023
|
5.302
|
.615***
|
EPS
|
<-->
|
FP
|
.138
|
.031
|
4.491
|
.408***
|
CC
|
<-->
|
PSM
|
.125
|
.027
|
4.595
|
.688***
|
CC
|
<-->
|
FP
|
.154
|
.036
|
4.234
|
.488***
|
PSM
|
<-->
|
FP
|
.127
|
.027
|
4.790
|
.543***
|
EPS
|
<-->
|
GC
|
.189
|
.040
|
4.790
|
.627***
|
CC
|
<-->
|
GC
|
.167
|
.041
|
4.019
|
.590***
|
PSM
|
<-->
|
GC
|
.116
|
.028
|
4.161
|
.555***
|
FP
|
<-->
|
GC
|
.183
|
.045
|
4.109
|
.503***
|
Note: *** mean p<0.0001, CC: communication and collaboration, CR: composite reliability, EPS: education on patient safety, FP: facilities in the practice, GC: generic conditions, PSM: patient safety management