Psychometric properties of questionnaire scales to assess psychosocial determinants that influence compliance with surgical site infection prevention measures: results of the WACH-study

doi:10.21203/rs.3.rs-3199535/v2

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Psychometric properties of questionnaire scales to assess psychosocial determinants that influence compliance with surgical site infection prevention measures: results of the WACH-study

https://doi.org/10.21203/rs.3.rs-3199535/v2

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Objective: Psychosocial determinants influence healthcare workers’ compliance with infection prevention and control measures. In order to design needs-based interventions promoting compliance, such determinants must first be assessed using valid and reliable questionnaire scales. To compare professional groups without bias, the scales must also be measurement-equivalent. This study examines these psychometric properties of four questionnaire scales that assess determinants influencing compliance with surgical site infection (SSI) prevention measures. Exploratively, associations with self-reported compliance are reported.

Design: Cross-sectional survey.

Setting: Nine general/visceral or orthopedic/trauma surgery departments in six non-university hospitals in Germany.

Participants: N=90 physicians and N=193 nurses (response rate: 31.5%).

Methods: A written questionnaire was used to assess the compliance with SSI prevention measures and the determinants of compliance based on the Capability-Opportunity-Motivation-Behavior-Model. For psychometric testing, single- and multiple-group confirmatory factor analyses were performed, and t-tests and multiple linear regressions for explorative analyses.

Results: The scales assessing individual determinants of compliance (capability, motivation, planning) were found to be reliable (Cronbach’s αs≥.85) and valid (Root-Mean-Square-Error of Approximation=.065, Comparative-Fit-Index=.95) and revealed measurement equivalence for physicians and nurses. The scale assessing external determinants (opportunity) did not demonstrate validity, reliability, or measurement equivalence. Group differences were found neither in compliance (p=.627) nor determinants (p=.129; p=.866; p=.964). Capability (β=.301) and planning (β=.201) showed theory-conform associations with compliance for nurses only.

Conclusions: The scales assessing motivation, capability, and planning regarding SSI prevention compliance provided reliable and valid scores for physicians and nurses in surgery. Measurementequivalence allows group comparisons of scale means to be interpreted without bias.

Infection prevention and control

Surgical site infections

Compliance

Nurses

Physicians

COM-B

Measurement equivalence

Confirmatory factor analysis

Multiple-group confirmatory factor analysis

Healthcare worker (HCW) compliance remains a key bottleneck in the successful implementation of evidence-based infection prevention and control (IPC) interventions. For example, although hand hygiene compliance (HHC) is consistently higher among nurses than physicians, it is still suboptimal.^1-3 Thus, attempts were made to understand possible reasons for (non-)compliance using behavioral science approaches, among others.^4,5 One promising strategy is to tailor implementation interventions, such as trainings, to HCW and their working environment, as this significantly increases the effectiveness of interventions.^6,7 Therefore, psychosocial determinants that influence compliance should be identified prospectively and then used to design implementation interventions.

For instance, two studies^8,9 have developed questionnaire items to measure such determinants on HCW’s HHC using the Capability-Opportunity-Motivation-Behavior (COM-B) model.¹⁰ According to this model, there are three constructs that influence behavior: physical and psychological capabilities, social and physical environmental factors (opportunities), and conscious and unconscious motivational processes.¹⁰

Both studies used pooled data from physicians and nurses to assess the psychometric properties of the newly developed questionnaires. A scale score is considered reliable if it reflects the true value (as closely as possible), meaning, if measurement errors are as small as possible. Usually Cronbach’s α is used as an indicator of reliability. Validity, defined as whether the items really measure the underlying, non-measurable constructs, is usually examined by confirmatory factor analysis (CFA). Validity is observed, for instance, if highly motivated HCW actually display high mean scores on the "motivation" scale.

However, CFA results of pooled data from different subgroups, such as physicians and nurses, do not reveal whether these groups understand questionnaire items in an equivalent manner. As an extension to validity testing, measurement equivalence (ME; synonym: measurement invariance) ensures that item comprehension is comparable across subgroups and that underlying constructs are interpreted the same in each subgroup.¹¹ For instance, if there is ME, a nurse and a physician with the same level of motivation would respond similarly to items measuring “motivation”. Thus, ME strictly speaking is a prerequisite for ensuring that comparisons of means are unbiased and reflect reality.^12,13 ME is usually tested by multiple-group confirmatory factor analysis (MGCFA), an extension of CFA.¹⁴

To explore the ME of physicians and nurses in COM factors, this study follows up on a previous study in which the COM-B model was applied to compliance with surgical site infection (SSI) prevention measures of orthopedic physicians in a tertiary care university clinic.¹⁵ In this previous study, exploratory factor analysis of 12 items on individual COM determinants produced three scales covering the constructs of “capability“, “motivation“, and “planning“. Another exploratory factor analysis of six items on environmental determinants resulted in one scale, “opportunity”.¹⁵ In the present analysis, we determine the validity, reliability and ME of these scales¹⁵ using data from physicians and nurses from outside the university sector. Additionally, we explore associations between valid, reliable, and cross-group equivalent scale scores and self-reported SSI prevention compliance.

From March to October 2019, a cross-sectional survey was conducted as part of the baseline data collection of the WACH trial (“Wundinfektionen und Antibiotikaverbrauch in der Chirurgie” [“Wound Infections and Antibiotic Use in Surgery”]) using a self-administered written questionnaire.¹⁶ The WACH study used the behavioral approach of a previous university hospital study¹⁷ to promote compliance with SSI prevention measures in six non-university hospitals.¹⁶ The study was approved by the Ethics Committee of the Medical Faculty of the University of Leipzig (vote no. 034/18-ek) and the ethics committees of the medical associations of the participating federal states. The baseline survey was approved by the staff councils of all participating hospitals.

The IPC team at each hospital distributed the questionnaire to physicians and nurses working in either the general/visceral surgery, orthopedic/trauma surgery, or anesthesia department. The duration of data collection ranged from 14 to 97 days depending on the size of the hospital, the number of participating departments, and the work capacity of the IPC team. Informed consent was obtained from all participants.

The questionnaire included 94 items addressing compliance with 26 clinical SSI prevention measures, COM determinants, socio-demographics, and awareness of in-house implementation interventions (not reported here). The COM determinants were surveyed using 18 items on a 7-point-Likert-scale ("strongly disagree" to "strongly agree"). In accordance with the previous EFA,¹⁵ number of items measuring “capability”, “opportunity”, “motivation”, and “planning” were six, six, four, and two, respectively (for items, see Supplementary file 1). The SSI prevention measures for which self-reported compliance was assessed were selected in accordance with national recommendations (see Supplementary file 2). For each of the measures, participants were asked to indicate in what percentage of the indication moments they performed the measure compliantly. Participants could select "not applicable" if they felt a measure was outside their area of responsibility. The individual overall compliance rate was calculated as in ¹⁵: The sum of all compliance rates divided by the number of measures for which responsibility was indicated.

Participants indicated their gender, age, and specialty. Based on the latter, participants were categorized as physician or nurse. Age was recorded in categories (<18, 18–30, 31–40, 51–60, >60 years) to comply with data protection regulations.

IBM SPSS Statistics v27 was used for t-tests and regressions, and R’s lavaan-package v0.6-12 for CFA and MGCFA. To account for missing data and multivariate normality assumptions, a robust full-information-maximum-likelihood estimate was used.^19,20 Two models were tested based on the earlier EFA:¹⁵ one with the three individual determinants “capability”, “motivation”, and “planning”, and one with the environmental determinants, (“opportunity”).

CFA was used to test for construct validity with a good model fit determined by Root-Mean-Square-Error of Approximation (RMSEA) ≤.08,²¹ Standardized Root Mean Square Residual (SRMR) ≤.08,²² and Comparative-Fit-Index (CFI) ≥.9.²³ The reliability of the scales was tested using Cronbach's α, with values >.7 being considered acceptable.²⁰

In case of satisfactory model fit in CFA, MGCFA was used to test for ME across physicians and nurses. To perform MGCFA, the assumption of sufficient model must be met in both groups, therefore stratified CFAs were performed beforehand.¹²

ME is not dichotomous but has three hierarchical levels.^11,12 During the steps of MGCFA the model is increasingly constrained, i.e. forced to be equal across both groups.¹² Configural equivalence is the lowest level and achieved when the model fit is good despite the number of constructs and the pattern of items loading on constructs being the same in both groups.¹² The next higher levels, metric and scalar equivalence, are confirmed if the respective model fit does not decrease compared to the respective less constrained model, with ∆CFI <.01 as cut-off.^11,12 For testing metric equivalence, factor loadings, that is the correlation between item and construct, are constrained, implying that the constructs’ relationships within the model can be compared across groups.^11,12 For testing scalar equivalence, item intercepts are additionally constrained, implying that cross-group comparisons of constructs’ means are possible.^11,12

Based on results of CFA and MGCFA, further explorative analyses were conducted. To compare physicians and nurses, t-tests were performed for the compliance index, COM scales with ME, and the number of SSI prevention measures participants considered to be responsible for (“responsibility”). Multiple hierarchical linear regressions were performed to examine associations between COM scales with ME and overall compliance. In the first regression model in each case, “responsibility” and participants’ specialty (surgery vs. anesthesiology) were entered as confounders. “Motivation” was added in the second model, followed by “capability” in the third model. As a specific factor that had emerged in the earlier study,¹⁵ “planning” was added in the final model.

N=336 HCW participated (response rate: 31.5%). For N=18 participants the information on specialty was missing, implying response rates of 28.4% for physicians and 30.4% for nurses. Furthermore, N=3 physicians and N=32 nurses were excluded because they answered less than 20% of the COM items. The analysis sample consisted of N=90 physicians and N=193 nurses (see Table 1 for sample characteristics).

Table 1 about here

For the one-factor model of environmental determinants, CFA revealed insufficient model fit (Χ²=86.6, df=9, p<.001, RMSEA=.175, SRMR=.067, CFI=.83), precluding consideration of this scale in further analysis.

For the three-factor model of individual determinants, CFA revealed an excellent model fit (Χ²=111.3, df=51, p<.001, RMSEA=.065, SRMR=.039, CFI=.95), with reliability for “capability” (Cronbach’s α=.90), “motivation” (α=.85), and “planning” (α=.89; see Figure 1). Criteria for MGCFA were met, thus ME testing was performed. As Table 2 shows, MGCFA revealed both metric and scalar equivalence, as the decline in model fit was tolerable (∆CFI=−.006 and ∆CFI=.005, respectively).

Figure 1 about here

Table 2 about here

Results of t-tests are presented in Table 3. Physicians and nurses did not differ in terms of self-reported compliance or individual determinants. However, physicians reported a significantly higher number of measures under their responsibility than nurses (p<.001).

Table 3 about here

Results of the linear regression models are shown in Table 4. For physicians, the final model explained 6% of the variance in self-reported compliance (F_(5;79)=2.08, p=.077). Being a surgeon (vs. being an anesthetist) was the regressor with the highest standardized coefficient (β=.196), but like all others, it was not statistically significant (p=.123).

Table 4 about here

For nurses, the first model (“specialty” and “responsibility”) explained 10.2% of the variance. The final model explained 25.4% of the variance (F_(5;164)=12.52, p<.001) and “capability” (β=.301, p=<.001) and “planning” (β=.201, p=.006) showed the strongest associations.

The scales "motivation", "capability" and "planning", which were created to assess the individual determinants of compliance with SSI prevention measures,¹⁵ proved to be valid and reliable for both physicians and nurses. Neither validity nor reliability could be demonstrated for the scale to assess the environmental determinants. Due to the sample recruitment in six hospitals, the data has a hierarchical structure. Presumably, the assessment of the environmental determinants was influenced more by hospital-specific characteristics than that of the individual determinants. This could have led to the participants' responses to the external determinants being more inconsistent across all hospitals. However, due to the sample size, the CFA could not be conducted in subgroups per hospital. As previous studies have emphasized the importance of environmental determinants of IPC compliance,^24-27 developing valid and reliable measures of environmental determinants remains an important task.

Whereas in original COM-B terminology, planning represents a facet of motivation,¹⁰ CFA results replicated “planning” as an independent construct.¹⁵ Action and coping planning, a cognition oriented toward the implementation of behavior, seems to stand out as a specific determinant of (at least self-reported) behavior, especially given the large number of SSI prevention measures. In addition, the mean values for "planning" for physicians and nurses were by far the lowest of all the scales (see Table 3 and Supplementary_file_1). It appears that HCW have high intentions to comply with SSI prevention guidelines, but do not adequately plan how to translate this motivation into actual behavior. To help HCW overcome this intention-behavior gap,^28-29 tailored implementation interventions may specifically address action and coping planning to promote compliance. In general, we emphasize that this result should not be considered as an extension of the COM-B model, even though the labels "motivation" and "capability" have been retained for the other factors representing individual determinants.

Unlike previous studies,^24-27,30 physicians and nurses in this sample did not differ in psychosocial determinants of compliance. By identifying ME between physicians and nurses, comparisons of scale means between these groups are unbiased and can be meaningfully interpreted. This raises the question of whether the differences found in previous studies are spurious.³¹ In other words, if questionnaires with no or inadequate ME have been used, any differences found in the determinants of compliance may be due to different understandings of the items and may not reflect true differences in the underlying construct. On the other hand, existing differences may not be found due to a lack of, or insufficient, ME. This supports the call for greater attention to issues of construct validity in IPC research,³² not only in measuring compliance, but also in measuring the determinants of compliance.

Although physicians and nurses did not differ in self-reported compliance rate, differences were found in the regressions modeling self-reported compliance. For physicians, the explained variance of compliance was <10% and no regressor showed a significant relationship. In contrast, for nurses, the final model explained over 25% of the variance, and "planning" and "capability" showed a significant influence. Considering that self-reported compliance was similar in both groups and that observed compliance is consistently lower among physicians,^1-3,33these differences in determinants predicting compliance can be interpreted in three different ways. First, the determinants may be less predictive of physicians' self-reported compliance because their overestimation is greater than that of nurses,³³ and thus these self-reports do not reflect compliance but rather overestimation. Second, the individual facets of the COM-B model may simply be not that relevant to physicians’ compliance, at least when measured with the scales used here. This would be consistent with the limited success of previous research in promoting physician IPC compliance,^1-3,7,17 and raising the question of which factors are relevant to physicians. Third, there is the possibility that things are more chaotic than theory suggests, that is highly context-specific and/or dependent on unknown factors. This would be supported by the fact that the regression results for physicians in a university orthopedic hospital¹⁵ were similar to those for nurses in this non-university sample. The 'chaos' may highlight the need to tailor any intervention to promote compliance to the situation 'on the ground' in an even more careful way. This could be demonstrated by studies linking data on psychosocial determinants to observed compliance at the individual level of each HCW. However, such designs face ethical and data protection barriers.

This study has several strengths, including being confirmatory not only by its statistical approach but also by replicating previous analyses¹⁵ and, to our knowledge, being the first study examing the ME of psychosocial determinants of compliance with IPC measures.

Nevertheless, the study has some limitations. To begin with, the survey response rate was just above 30%, which was lower than the global average of 53% among surgical physicians.³⁴ This led to a nearly undersized sample for the MGCFA among physicians. Probably questionaire length, as well as the lack of tangible incentives and questionnaire personalization,³⁵ affected the response rate. The earlier study achieved a response rate of 73% among surgical physicians, likely due to the fact that healthcare professionals in a university hospital demonstrate a higher affinity for research and the study received exceptional support from the medical director.¹⁵ While efforts should be made to increase response rates, there is a declining trend in response rates among surgical physicians,³⁵ and the current rate is at least within one standard deviation of the 2019 mean.³⁵

Second, the 18 COM items represent only a sample of facets of all compliance determinants. For example, the Theoretical Domains Framework, the most integrative and granular classification of theoretical constructs relevant to behavior change, maps 84 constructs into 14 domains.³⁶Nevertheless, all of these 84 constructs can be mapped onto the COM components.³⁶ The present items therefore reflect the need for a concise survey instrument. The selection of the most relevant facets was based on the best available evidence and the expertise of the transdisciplinary WACH study group.

Third, the items used to collect the determinants referred to all prevention measures within the participant's area of responsibility. It could be argued that this may bias the relationship between the determinants and the overall compliance score. Models such as the COM-B model were developed to identify determinants of individual behaviors in order to design behavior change interventions. However, different determinants may be important for different behaviors.¹⁰ While this argument certainly holds validity, a key trend in SSI prevention has been the use of bundles,^37-39 i.e., sets of “…evidence-based interventions...that, when implemented together, will result in significantly better outcomes than when implemented individually”.^{40,p. 2} Compliance is therefore increasingly defined and measured on the basis of more than one measure. In this sense, the operationalization of SSI prevention compliance used in this study may be seen as a kind of "subjective bundle" approach. However, we acknowledge that such bundles may need unbundling to describe specific interventions to promote compliance.

Fourth, no specific direction of causality could be established because of the cross-sectional design of this observational study. Thus, the use of the scales in longitudinal studies is necessary to assess their causal structure and sensitivity to change.

Finally, the data were collected before the COVID-19 pandemic. Physicians and nurses may now differ in self-reported compliance and COM factors. However, the results of CFA and MGCFA, i.e., the evidence base for the validity, reliability, and ME of the scales, should be robust because these statistical analyses are based on the structure of the item responses, not the item scores themselves.

In summary, this study provides scales to assess capability, motivation, and action and coping planning as individual determinants of compliance to SSI prevention measures. As scales with proven ME among physicians and nurses, they allow a valid interpretation of group comparisons, at least for the original German version. Thus, they contribute to the understanding of how and why healthcare professions (do not) differ in IPC compliance. In conclusion, the study contributes to physicians’ IPC compliance as a current key topic in this area⁷ and meets the demand for “…easy-to-use, valid tools to provide data on the factors that influence the…behavior of healthcare professionals”.^{8, p. 412}

COM-B: Capability-Opportunity-Motivation-Behavior Model
CFI: Comparative-Fit-Index
CFA: confirmatory factor analysis
EFA: exploratory factor analysis
HCW: healthcare workers
HH: hand hygiene
IPC: infection prevention and control
ME: measurement equivalence
MGCFA: multiple group confirmatory factor analysis
RMSEA: Root-Mean-Square-Error of Approximation
SD: standard deviation
SRMR: Standardized Root Mean Square Residual
SSI: surgical site infection

WACH: Wundinfektionen und Antibiotikaverbrauch in der Chirurgie (Surgical Site Infections and Antibiotic Use in Surgery) = acronym and name of the study

Acknowledgements

Financial support. This research was conducted as part of the research project WACH (“Wundinfektionen und Antibiotikaverbrauch in der Chirurgie” [“Wound Infections and Antibiotic Use in Surgery”]), which was funded by the German Federal Ministry of Health from January 15, 2017 to February 11, 2021 within its program “Antibiotic Resistance and Nosocomial Infections” (Grant ID: ANNIE2016-55-038). WACH was registered in the German Clinical Trial Register (DRKS; https://www.bfarm.de/EN/BfArM/Tasks/German-Clinical-Trials-Register/_node.html/). The funding body had no other roles than the funding itself (i.e., the authors were independent in the design of the study and collection, analysis, and interpretation of data and writing the manuscript).

Conflict of interest. The author(s) declare none.

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Table 1. Sample description by gender, age, specialty and department

		Physicians N = 90		Nurses N = 193		Total N = 283
Gender
female	N (%)	41	(46.1%)	155	(82.4%)	196	(70.8%)
male	N (%)	48	(53.9%)	33	(17.6%)	81	(29.2%)
Age (in years)
< 18	N (%)	−		3	(1.1%)	3	(1.1%)
18 − 30	N (%)	13	(14.8%)	51	(27.6%)	64	(23.4%)
31 − 40	N (%)	28	(31.8%)	48	(25.9%)	76	(27.8%)
41 − 50	N (%)	23	(26.1%)	36	(19.5%)	59	(21.6%)
51 − 60	N (%)	18	(20.5%)	39	(21.1%)	57	(20.9%)
> 60	N (%)	6	(6.8%)	8	(4.3%)	14	(5.1%)
Specialty
Surgical specialist	N (%)	42	(46.7%)	−		42	(14.8%)
Surgical resident	N (%)	19	(21.1%)	−		19	(6.7%)
Anesthesiology specialist	N (%)	14	(15.6%)	−		14	(4.9%)
Anesthesiology resident	N (%)	13	(14.4%)	−		13	(4.6%)
Physician assistant	N (%)	2	(2.2%)	−		2	(0.7%)
Ward nurse	N (%)	−		97	(50.3%)	97	(34.4%)
Perioperative nurse	N (%)	−		62	(32.1%)	62	(21.9%)
Anesthesiology nurse	N (%)	−		34	(17.6%)	34	(12.0%)
Department
Orthopedics/Trauma surgery	N (%)	37	(41.1%)	71	(36.8%)	108	(38.2%)
General/Visceral surgery	N (%)	21	(23.3%)	53	(27.5%)	74	(26.1%)
Both surgical departments	N (%)	5	(5.6%)	35	(18.1%)	40	(14.1%)
Anesthesiology	N (%)	27	(30.0%)	34	(17.6%)	61	(21.6%)

Note: Any data not adding up to the relevant total due to missing values.

Table 2. CFA and MGCFA results of measurement equivalence testing

	Χ²	p	df	RMSEA (90% CI)	SRMR	CFI	∆CFI^a
One-factor model of external determinants
CFA
Total	86.6	< .001	9	.175 (.145; .206)	.067	.83
MGCFA^b
Three-factor model of internal determinants
CFA
Total	111.3	< .001	51	.065 (.050; .079)	.039	.95
Physicians	97.8	< .001	51	.101 (.073; .128)	.052	.93
Nurses	97.7	< .001	51	.069 (.050; .088)	.041	.94
MGCFA
Configural equivalence	195.5	< .001	102	.081 (.065; .096)	.045	.935
Metric equivalence	195.8	< .001	111	.073 (.058; .089)	.047	.941	− .006
Scalar equivalence	211.4	< .001	120	.073 (.058; .088)	.050	.936	.005

Notes: CFA: confirmatory factor analysis; MGCFA: multiple-group confirmatory factor analysis;
Χ²: Χ²-test statistic, p: p-value; df: degrees of freedom; RMSEA: Root-Mean-Square-Error
of Approximation; SRMR: Standardized Root Mean Square Residual; CFI: Comparative-Fit-Index.
^a ∆CFI was only calculated during MGCFA
^b MGCFA was not performed on the one-factor model due to insufficient model fit in CFA

Table 3. Results of t-tests for comparing physicians and nurses.

	Nurses			Physicians			t	df	p
	N	x̄	SD	N	x̄	SD
Compliance (%)	193	88.3	9.8	90	88.8	7.5	.486	281	.627
Capability	186	5.6	1.0	86	5.7	1.1	−1.312	153.7	.192
Motivation	185	6.2	.9	89	6.2	1.1	.169	138.5	.866
Planning	184	3.9	1.7	89	3.9	1.8	−.045	165.1	.964
Responsibility	193	10.1	4.7	90	17.1	4.9	10	167.5	<.001

Notes: x̄: mean value; SD: standard deviation; t: t-test statistic, p: p-value; df: degrees of freedom.

Table 4. Results of hierarchical multiple linear regressions for physicians and nurses.

		Nurses (N=170)				Physicians (N=85)
Model		1	2	3	4	1	2	3	4
	F	10.63	10.96	13.18	12.52	2.92	2.78	2.53	2.08
	corr R sq	.102	.150	.224	.254	.044	.060	.068	.060
	p	<.001	<.001	<.001	<.001	.060	.047	.047	.077
(Constant)
	t	45.84	12.29	11.22	11.55	23.45	13.31	12.77	12.71
	p	<.001	<.001	<.001	<.001	<.001	<.001	<.001	<.001
Surgeon (ref. anaesthesiologist)
	t	3.58	2.79	1.70	1.74	1.93	1.83	1.58	1.56
	β	.265	.207	.125	.126	.241	.227	.198	.196
	p	<.001	.006	.091	.084	.057	.070	.118	.123
Responsibility
	t	3.46	3.11	3.17	2.86	.25	.26	.09	‒.01
	β	.256	.225	.220	.196	.031	.033	.011	‒.001
	p	<.001	.002	.002	.005	.806	.792	.929	.996
Motivation
	t		3.23	.53	.24		1.55	.45	.32
	β		.237	.044	.020		.164	.060	.043
	p		.002	.602	.811		.126	.653	.754
Capability
	t			4.09	3.42			1.30	1.19
	β			.357	.301			.180	.166
	p			<.001	<.001			.196	.238
Planning
	t				2.78				.60
	β				.201				.071
	p				.006				.550

Notes: F: F-test statistic; corr R sq: corrected R square; p: p-value t: t-test statistic; β: standardized regression coefficient

No competing interests reported.

AdditionalFile1.docx
Supplementary File 1: Table A1. Means and standard deviations (SD) of items and scales measuring psychosocial determinants of SSI-preventive compliance
AdditionalFile2.docx
Supplementary File 2: Table A2. Means and standard deviations (SD) of self-reported compliance with SSI-preventive clinical interventions for which participants reported responsibility for

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Psychometric properties of questionnaire scales to assess psychosocial determinants that influence compliance with surgical site infection prevention measures: results of the WACH-study

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