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Health Education Research Advance Access originally published online on June 1, 2006
Health Education Research 2007 22(1):14-26; doi:10.1093/her/cyl046
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

Identifying determinants of protocol adoption by midwives: a comprehensive approach

Dewi Segaar1,*, C Bolman2, MC Willemsen3 and H De Vries1

1 Department of Health Promotion and Health Education, University of Maastricht, Maastricht 6200 MD, The Netherlands
2 Open University of the Netherlands, Heerlen 6401 DL, The Netherlands
3 STIVORO for a smoke free future, The Hague 2508 WB, The Netherlands

* Correspondence to: D. Segaar, PO Box 16070, 2500 BB The Hague, The Netherlands. E-mail: d.segaar{at}gvo.unimaas.nl


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Conflict of interest statement
 Acknowledgements
 References
 
Adoption of potentially effective preventive interventions often fails. This study aimed to identify factors that determine why midwifery practices decide to use a smoking cessation protocol, using a comprehensive model of both organizational and psychosocial factors. A cross-sectional survey was conducted among representatives of all 446 Dutch midwifery practices, of whom 251 (56%) responded. The results show that adoption of the protocol was facilitated by the presence of practice assistants and impeded by a large proportion of clients of foreign ethnic origin. The most successful information channel was the midwives' professional association. A consistent positive attitude (perceiving a lot of advantages, few disadvantages and a low level of ambivalence) and positive social norms toward using the protocol, a perceived large proportion of midwives who use the protocol and knowledge about the protocol significantly increased the likelihood of adoption. The decision to use the protocol was better explained by personal awareness and motivation factors than by organizational factors.


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Conflict of interest statement
 Acknowledgements
 References
 
Smoking during pregnancy causes several risks to the unborn, such as low birth weight and fetal death [1, 2]. Smoking cessation is important to reduce negative pregnancy outcomes. Smoking cessation programs provided during pregnancy have proved to be efficacious in reducing smoking among pregnant women [3, 4]. In the Netherlands, midwives assist ~75% of all pregnant women [4], and for most women, midwives are the main source of information on pregnancy-related subjects. Women visit their midwives at least once a month during a regular pregnancy. Thus, midwives are regarded as ideal intermediaries to provide pregnant women with smoking cessation assistance. More than 90% of all Dutch midwives work in group practices of about three midwives. About half of them have practice assistants who can take over some tasks [5].

Till recently, midwives in the Netherlands usually did not pay systematic attention to smoking. Most midwives discussed smoking with their clients, but only to a limited extent [6]. Midwives indicated that they were motivated to provide smoking cessation counseling, but they did not feel sufficiently capable to provide the counseling [7]. For this reason, an effective smoking cessation and relapse prevention protocol for midwives has been developed in the Netherlands [8]. The protocol is called the Minimal Intervention Strategy for Smoking Cessation (MIS) in midwifery practices. It is a stepwise strategy that focuses on the motivational level of pregnant women. It is comparable with the Ask, Advice, Assess, Assist and Arrange (five A's) model, which is recommended in Smoking Cessation Guidelines for Health Professionals [9, 10].

The Dutch Expert Center on Tobacco Control (STIVORO) has distributed the protocol among midwifery practices in the Netherlands in cooperation with the Royal Dutch Organization for Midwives through various channels, such as direct mailing, professional journals and conferences. Training courses on how to use the MIS have been offered, and relevant materials, such as manuals and an information magazine for pregnant women, have been provided as well. Since the adoption of potentially effective preventive interventions in the target field often fails [11], the question arises whether midwives actually do adopt the protocol, and why.

Adoption has been studied by various disciplines, ranging from business marketing to psychology. These studies have identified a wide variety of factors influencing adoption, such as organization size [12, 13], group membership [13] and innovation characteristics [14]. However, these results are difficult to extrapolate to other settings because context-specific factors appear to be involved [15, 16]. Consequently, research is needed to identify reasons for adoption in specific settings and specific populations.

The aim of our study was to identify organizational characteristics and personal (psychological) characteristics of the decision maker that determine the adoption of health education innovations by midwifery practices. More specifically, the study focused on the adoption of the MIS. We aimed to identify the factors that had the strongest associations with adoption of this specific intervention and studied differences between three adoption groups: non-intenders, intenders and users.

Theoretical model
To assess the determinants of adoption of the MIS by midwifery practices, the present study used the Integrated Change (I-Change) Model [17, 18], which integrates concepts of various social-cognitive models [19–22]. The model is presented in Fig. 1.


Figure 1
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  		Fig. 1. The I-Change Model [17, 18] applied to the adoption of preventive interventions by midwifery practices.

 
The I-Change Model states that behavior is determined by intention. Transition from the intention state to the behavioral state is determined to an important extent by the ability to perform the behavior and by barriers. Since adoption takes place at the intention stage, the post-motivation factors in the I-Change Model, i.e. ability factors and barriers, were not examined in the current study. Intention state is determined by three types of proximal ‘motivation factors’: attitude, social influence and self-efficacy. Attitude includes the beliefs about the innovation characteristics defined by Rogers [23], namely, perceived relative advantage, compatibility, complexity, trialability and visible results. These factors have already been used to explain the adoption of various types of health behavior, like classroom-based education [24], use of hormone replacement therapy [25] and physical activity behavior [26].

The I-Change Model assumes that the proximal motivation factors are determined by various more distal (pre-motivation) factors, such as ‘awareness factors’ (i.e. knowledge, cues to action and risk perceptions), ‘information factors’ (the quality of messages, channels and sources used) and ‘predisposing factors’ (i.e. behavioral factors, psychological factors, biological factors and social and cultural factors). To identify relevant social and cultural factors, we used a contingency model of strategic decision making from the organizational psychology perspective, developed by Koopman and Pool [27, 28]. The applicability of this model in implementation research has been described by Willemsen et al. [29]. The model defines three major groups of characteristics influencing the decision-making process: the content of decision making, its context and the decision-making style that is dominant in the organization. The context of decision making consists of characteristics of the environment, of the organization and of the decision maker. Characteristics of the decision maker belong to the behavioral and biological factors in the I-Change model.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Conflict of interest statement
 Acknowledgements
 References
 
Study design and procedure
A cross-sectional nationwide study was conducted in 2003; 2 years after the nationwide introduction of the MIS in the Netherlands had started. The study focused on all midwifery practices in the Netherlands (N = 446). One written questionnaire was mailed to each practice, with the request to have it filled out by one of the midwives officiating as a representative of the practice. A letter explaining the aim of the study and a postage-paid return envelope were enclosed. To stimulate practices to return the questionnaire, the support of the national organization for midwives and the value of the opinion (maybe even more if negative) of each practice were stressed. As a reward, respondents could win a {euro}25 value coupon if they returned the questionnaire. Reminders were sent to the non-responders 3 weeks later.

Questionnaire
The questionnaire was based on existing questionnaires or relevant scales and on information from literature reviews and earlier work [7, 30–32].

Outcome variable
To be able to categorize respondents in terms of their ‘adoption’ state, three questions were asked. First, midwives were asked if they were already using the MIS in their practice (yes/no). If the answer was ‘no’, the second question was whether they had made a conscious decision about using the MIS at all (yes/no), followed by the third question ‘Do you intend to use the MIS in the future and if so when?’ (i.e. in the next month, in the next 6 months, in the next year, ever but not within a year or never).

Respondents were categorized into three groups according to their adoption state. The first group consisted of ‘non-intenders’. These were respondents who were not using the MIS and were not seriously planning to start using it in the near future. Near future was defined as 6 months from the time of measurement. The second group was that of ‘intenders’, respondents who were not using the MIS yet, but were seriously planning to use it in the near future. The third category was ‘users’, respondents who were already using the MIS protocol at their practice.

Predisposing factors
Detailed information on the questions is provided in Table I. ‘Biological factors’ measured were the age and gender of the midwives. ‘Behavioral factors’ assessed were the smoking behavior of the midwives and the number of years they had been working in their profession. ‘Social and cultural factors’ were assessed by questions relating to the environmental and organizational context and the style of decision making.


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  		Table I. Means of predisposing factors and information factors for non-intending, intending and using practicesa, and significant differences

 
Environmental ‘context’ characteristics included the proportion of clients of foreign ethnic origin and the proportion of clients with a low educational level. Measurements of ‘context’ variables at the organizational level included six items, i.e. the number of midwives working in the practice, whether there were practice assistants (yes/no), the number of pregnancies monitored per year, the time available for the first visit (minutes), workload and the innovativeness of the midwifery practice. Style of decision making was measured by five items: centralization (decisions entirely made by the individual midwives or centralized at practice level), formalization of decision making in the practice (own solutions or set procedures), the amount of information gathered preceding decisions, the proportion of external information sources versus internal information sources consulted for decisions and the way disagreements are solved.

Information factors
Detailed information on the questions about information factors is provided in Table I. For the five most important information sources on the MIS, midwives were asked if they consulted them.

Awareness factors
Detailed information on the questions about awareness is provided by Table II. ‘Awareness knowledge’ was measured by four questions: ‘Have you ever heard of the MIS?’, ‘Do you know what the MIS is?’, ‘Do you know how the MIS works?’ and ‘Can you overlook the implications of using the MIS?’ An index score was calculated by summing the number of times a midwife answered yes to these questions. ‘Risk perception’ was assessed by asking two questions about perceived risks of smoking for the baby. A risk perception scale (r = 0.59) was calculated by averaging the scores of the two items. ‘Cues to action’ were measured by one scale: personal relevance. Personal relevance was measured with four items and a personal relevance subscale (Cronbach's {alpha} = 0.70) was calculated by averaging the item scores.


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  		Table II. Means of and significant differences in awareness factors for non-intending, intending and using practices

 
Motivation factors
Detailed information on the questions on motivation factors is provided by Tables III, IV and V.


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  		Table III. Means of and significant differences in attitude items for non-intending, intending and using practices

 

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  		Table IV. Means of and significant differences in social influence items for non-intending, intending and using practices

 

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  		Table V. Means of and significant differences in self-efficacy items for non-intending, intending and using practices

 
‘Attitude’ (Table III) was assessed by three constructs: pros, cons and attitudinal ambivalence [33]. ‘Pros and cons’ about using the MIS were assessed by 25 questions, including 11 items on beliefs about the innovation characteristics [23, 30–32, 34] and 14 items on perceived health benefits and other outcome expectations [20] of using the MIS protocol [8]. A factor analysis with oblimin rotation on the attitude variables revealed two attitude factors, viz. the pros (15 items, {alpha} = 0.90) and the cons (10 items, {alpha} = 0.79) of using the MIS protocol. ‘Attitude ambivalence’ was measured by two questions, one scale (r = 0.73) was calculated by averaging the two item scores.

‘Perceived social influences’ (Table IV) were assessed using three constructs: perceived social support, perceived social norms and social modeling. Perceived support and norms were assessed for six subgroups (see Table IV). For each subgroup, ‘support’ was measured by asking ‘Do you perceive discouragement or support to work with the MIS?’ and ‘social norms’ were measured by the question ‘Do you perceive positive norms toward your assisting pregnant women with smoking cessation according to the MIS?’ The scores for the subgroups were averaged to calculate one scale for support ({alpha} = 0.86) and one scale for norms ({alpha} = 0.88). ‘Social modeling’ was assessed by one question asking the respondent to estimate the percentage of other Dutch midwives in primary health care who use the MIS.

‘Self-efficacy expectations’ (Table V) were measured by asking midwives whether they would succeed in providing smoking cessation assistance with the MIS in 15 situations occurring in daily practice. Situations were derived from previous research on the adoption of smoking cessation programs in health care [8, 35]. A self-efficacy scale ({alpha} = 0.87) was calculated by averaging the scores for the 15 situations.

Statistical analyses
Univariate analyses of variance using the Tukey post hoc Honestly significant differences (HSD) contrasts [36] were used to test differences between non-intenders, intenders and users. Chi-square tests were used for dichotomous variables. The awareness and motivational variables were expected to change upon using the MIS. However, both intenders and users significantly differed from non-intenders on the scale scores for these variables. Hence, we combined intenders and users into one ‘adopter’ group to be able to identify the factors with the strongest association with adoption by performing logistic analyses with adoption as the dependent variable.

Three one-step logistic regression analyses were performed, using backward likelihood ratio procedures. In this type of analysis, all variables are initially included in the regression equation, then the variables that are judged by the model to be least important (based on the model's change in –2 log likelihood when removing the variable) are progressively removed from the model. This procedure continues as long as the removals do not significantly affect the fit of the model. The first analysis included all predisposing and information factors, in order to assess their relevance for adoption of the MIS. The second analysis added awareness factors in order to assess their additive contribution. In the third analysis, motivation factors were added, to identify their contribution and to identify which factors were strongest associated with adoption. Nagelkerke's R2 was used to approximate explained variances of the models [37].

Assumptions for regression analysis and a check on multicollinearity between the independent variables were found to be satisfactory. All analyses were performed using SPSS version 12.0.1. Differences were considered significant when P < 0.05.


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Conflict of interest statement
 Acknowledgements
 References
 
Description of sample
A total of 251 of the 446 practice representatives (56%) returned the questionnaire. Ninety-one respondents (36%) used the MIS (users), 42 respondents (17%) were planning to use the MIS (intenders) and 118 (47%) were not planning to use the MIS (non-intenders). Of the responding midwives, 98% were female; the mean age was 37 (SD = 10) years. Eight percent (n = 21) of the respondents smoked, and respondents had been working as a midwife for an average of 11 (SD = 9) years.

Differences between practices not intending to use, intending to use and using the MIS
The mean scores of non-intenders, intenders and users on the variables assessed are shown in Tables I–VGoGoGoGo.

Predisposing factors
Table I shows that users, intenders and non-intenders only differed on a few predisposing factors. Intenders gave a significantly lower estimate of the proportion of clients of foreign ethnic origin in their practice than non-intenders. Users and intenders were significantly more likely to have practice assistants than non-intenders. Users were significantly more likely to work in highly formalized settings than non-intenders.

Information factors
Table I shows that users were significantly more likely than non-intenders to have received information from the Royal Dutch Organization for Midwives and less likely to have received it from the Dutch Expert Center on Tobacco Control.

Awareness factors
Table II reveals that there were many significant differences between the three groups in terms of awareness factors. Both intenders and users had significantly more knowledge about the MIS than non-intenders. Users also had significantly more knowledge than intenders. Both users and intenders found the provision of smoking cessation assistance for pregnant women by midwives significantly more important than non-intenders. Users and intenders did not differ significantly from each other. Users perceived significantly greater risks of smoking for the unborn than non-intenders.

Motivation factors
Tables III, IV and V show that non-intenders differed significantly from both intenders and users in terms of all ‘motivation concepts’. Intenders and users did not differ significantly from each other on most motivation concepts, except modeling, one advantage subscale and a few self-efficacy items.

Differences and significance levels for each attitude item are shown in Table III. Intenders and users had a significantly more positive ‘attitude’ toward using the MIS than non-intenders. Intenders and users were more convinced of the positive outcomes, relative advantage and compatibility than non-intenders. Non-intenders regarded the MIS as more complex than intenders and users did. More so than non-intenders, intenders thought the visibility of the impact of the MIS was easy to assess. Users were more positive about the trialability of the MIS than non-intenders. Users and intenders were significantly less ‘ambivalent’ than non-intenders.

Table IV shows that both intenders and users perceived a significantly more positive ‘norm’ from their social environment than non-intenders. They also perceived more ‘support’ from their environment, especially from their colleagues and clients. Intenders and users both perceived a significantly larger proportion of midwives to use the MIS than non-intenders did. Users also expected a significantly larger proportion to use the MIS than intenders did.

Both users and intenders had a higher level of ‘self-efficacy’ about using the MIS than non-intenders, especially regarding the less problematic situations (see Table V).

Regression analyses
Predisposing and information factors that significantly increased the likelihood of adoption were the presence of one or more practice assistants (ß = –0.52, P < 0.01), having a small proportion of clients of foreign ethnic origin (ß = –0.39, P < 0.01), having received information from the Royal Dutch Organization of Midwives (ß = 0.42, P < 0.01) and not having received information from the Dutch Expert Center on Tobacco Control (ß = –0.30, P < 0.05). These factors explained 15% of the likelihood of adoption of the MIS (R2 = 0.15).

The second regression analysis showed that two awareness factors significantly increased the likelihood of adoption, namely, having much knowledge about the MIS (ß = 1.16, P < 0.01) and attaching importance to smoking cessation guidance for pregnant women (ß = 0.91, P < 0.01). These factors increased the likelihood of the adoption of the MIS to 48% (R2 = 0.48).

The third analysis showed the motivation factors that significantly increased the likelihood of adoption. These factors were a low level of ambivalence (ß = –0.84, P < 0.01), perceiving few disadvantages (ß = –0.72, P < 0.01), perceiving positive norms (ß = 0.66, P < 0.01), perceiving a high proportion of midwives to use the MIS (ß = 0.61, P < 0.05) and perceiving many advantages (ß = 0.52, P < 0.05). All factors together were associated with 69% of the variance in adoption of the MIS (R2 = 0.69). Except from the information factors, all the other variables remained significant next to the motivational factors.


   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Conflict of interest statement
 Acknowledgements
 References
 
The present study examined the determinants of the adoption of a minimal-contact smoking cessation intervention by midwifery practices in the Netherlands. Users, intenders and non-intenders were compared in terms of psychosocial and organizational characteristics. A comprehensive set of theory-based determinants was assessed.

Hardly any differences between users, i.e. those who had already adopted and were using the intervention, and intenders, i.e. those who had adopted the intervention but were not using it yet, were found. Intenders and users only differed significantly on the concepts knowledge and modeling. Users had significantly more knowledge on the MIS and perceived a higher proportion of midwives to use the MIS. Both intenders and users differed significantly from non-intenders on all awareness and motivational concepts, except risk perception.

To identify the factors with the strongest association with adoption, intenders and users were combined into one ‘adoption group’ and compared with the non-intenders. Significant predisposing and information factors explained only 15% of the likelihood of adoption. This might mean that we did not assess all the predisposing characteristics that distinguish between midwifery practices. It is also possible that the organizational characteristics that are most relevant to adoption are very similar in all midwifery practices. Personal awareness and motivation factors explained the likelihood of adoption much better than the predisposing organizational factors and information factors.

The most important predisposing factor that was found to facilitate adoption of the MIS was the availability of one or more assistants in the practice. One might think that the presence of assistants would result in lower workload and therefore facilitate adoption. However, correlations between the presence of assistants in a practice and amount of workload were very weak. This lack of association might be caused by the fact that assistants more often work in larger practices, where they especially perform organizing tasks that become more complex and time consuming when more midwives work in one practice [5].

Despite the fact that workload does not show to be associated with the presence of assistants, the factor time might still be related to the association between having assistants and adoption. Practice assistants might be able to reduce the necessary time investment of midwives when carrying out the protocol (the MIS); they can play a role in organizing and executing the MIS. Furthermore, they can motivate midwives to use the MIS in the practice. Unfortunately, we have no further information on this issue.

The second predisposing factor that was significantly related to adoption was the proportion of clients of foreign ethnic origin in the practice. A large proportion appeared to impede adoption. This may be due to the low self-efficacy of midwives toward using the MIS when clients have problems communicating in Dutch. It may also be caused by perceived cultural differences that keep midwives from talking about smoking. Another reason why language problems and cultural differences might impede adoption is that these factors might create more work and less time to learn about and incorporate smoking prevention activities. Only a small relation (r = 0.15, P < 0.05) between the proportion of clients of foreign origin and the proportion of clients with low educational levels was found.

A third predisposing factor, formalization, was not found to significantly increase the explained variance in adoption compared with a model with those two factors, but formalization increased among adoption stages and non-intenders and users differed significantly. This result partly contradicts with Rogers' theory, which states that formalization inhibits initiation in the innovation process [14]. However, our finding that more formalized practices not only adopted but also more often used the MIS than less formalized practices is in line with Rogers' view that formalization facilitates implementation of an innovation. One explanation may be that our study involved small organizations (mean size of 3.4 midwives), whereas Rogers refers to larger companies. This idea is supported by the findings of a comparable study on cardiac wards (mean size of 29 nurses), where formalization was indeed found to inhibit adoption of the MIS [13, 38]. The relative small size of the organization in our study might also have caused the lack of finding a positive association between size and adoption, which was found in several other studies [12, 13, 38, 39].

The Royal Dutch Organization for Midwives was the most effective information channel to disseminate information about the MIS. It is likely that this source is highly trusted by midwives, since 90% of all midwives are members of this organization [5]. Non-adopters were more likely than adopters to report that the Dutch Expert Center on Tobacco Control (STIVORO) provided them with information about the MIS. This difference can be attributed to the users. There were no differences between non-intenders and intenders. The lower likelihood of users to be informed by STIVORO may have been caused by the fact that this organization sent more direct mailing to non-users.

Two awareness factors were found to be significantly related to adoption, namely, knowledge and personal relevance. This finding is in line with the theory that the innovation-decision process starts with the steps knowledge and persuasion and so can be regarded as preterm for adoption [14].

Of the motivation factors, attitude was found to be very strongly associated with adoption. This is in line with the findings from several other studies [31, 32, 40]. The same is true for the positive association that we found between social influence and adoption [24, 25] and for the positive association between self-efficacy and adoption [24, 40–43]. However, self-efficacy did not significantly increase the explained variance in adoption in a model with other motivational factors. This is probably due to its effect on attitude.

The best associative model of adoption stated that the likelihood of adoption was highest when midwives had much knowledge about the MIS, they had a consistent positive attitude (perceiving a lot of advantages and few disadvantages and low level of ambivalence), they perceived positive social norms toward adoption and perceived a high proportion of midwives to use the MIS, they had one or more practice assistants and there was a small proportion of clients of foreign ethnic origin. This model was associated with 69% of the variance in adoption.

Before we can conclude that the factors in the model are the factors which should be targeted when trying to improve adoption strategies for Minimal Intervention Strategies or other comparable interventions by midwives, we should overview the limitations of the present study.

First, the study yielded only cross-sectional data, precluding causal inferences. Awareness and motivation characteristics may have been influenced by use of the intervention. However, our research only found differences between users and intenders on the concepts knowledge and modeling. If this difference was caused by using the MIS, the present study might overrate the effect of knowledge and modeling on adoption. Since both intenders and users had more knowledge and perceived a higher proportion of midwives to use the MIS than non-intenders, the existence of an important effect of knowledge and modeling on adoption is beyond dispute. No differences were found on any of the other variables. This suggests that most differences between adopters and non-adopters do not result from using the MIS, making it likely that there is a causal relationship between adoption and the determinants we found in this study.

Second, although we invited all midwifery practices in the Netherlands to participate, little more than half (57%) enrolled, which may have resulted in selection bias. We compared the proportion of MIS users among our responders with the proportion of MIS users in the total population, which is registered in an electronic database by the organization that coordinates the implementation of the MIS (the Dutch Expert Center on Tobacco Control). At the time of our study, the registered proportion of users in this database was 35%, while the proportion of users among our responders was 36%. This indicates that practices using the MIS were not overrepresented. However, the proportion of smokers among the responders was 8%, which is much smaller than the proportion of smokers among Dutch women in general (25% in 2004 [44]). Even though midwives have been found to smoke less than the general female population [45], the figure of 8% is small. This might indicate underrepresentation of smokers among the respondents, which could have caused an underestimation of the effect of smoking on the adoption of the MIS.

In conclusion, the study has provided important insights into the factors that play a role in the adoption of a protocol for smoking cessation guidance in midwifery practices. These results could be used to improve dissemination strategies and facilitate adoption. However, there is still much room for improvement in this field, by further increase of knowledge on predictors of adoption. Therefore, studies like the present one should also be conducted in other settings. Ideally, these will be studies with a longitudinal design, planned prior to the implementation of interventions, so that predictive rather than associative patterns can truly be identified.

Practice implications of our findings are that, independent of what organizational characteristics occur, dissemination strategies should primarily focus on the awareness and motivation factors. Information should be disseminated to increase knowledge about the MIS. The positive attitude toward the MIS should also be stimulated by increasing the perception of advantages and by promoting the conviction that there are no disadvantages. The best way to disseminate information on the MIS and its advantages is via existing sources that are trusted by midwives, like professional associations. Increasing the perception of advantages and decreasing that of disadvantages will also result in a desirable decrease in ambivalence. Furthermore, dissemination strategies should try to increase the positive perception of the social norms and the perceived number of other midwives using the MIS, for example, by stimulating peer contacts on the subject.


   Conflict of interest statement
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflict of interest statement
Acknowledgements
 References
 
None declared.


   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflict of interest statement
 Acknowledgements
References
 
The authors like to thank the midwives who participated in the present study. This study was supported by grants from the Netherlands Organization for Health Research and Development.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Conflict of interest statement
 Acknowledgements
 References
 
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Received on October 27, 2005; accepted on April 28, 2006


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