Knowledge translation in Uganda: A qualitative study of Ugandan midwives’ and managers’ perceived relevance of the sub-elements of the context cornerstone in the PARIHS framework

Background: A large proportion of the annual 3.3 million neonatal deaths could be averted if there was a high uptake of basic evidence-based practices. In order to overcome this ‘know-do’ gap, there is an urgent need for in-depth understanding of knowledge translation (KT). A major factor to consider in the successful translation of knowledge into practice is the influence of organizational context. A theoretical framework highlighting this process is Promoting Action on Research Implementation in Health Services (PARIHS). However, research linked to this framework has almost exclusively been conducted in high-income countries. Therefore, the objective of this study was to examine the perceived relevance of the sub-elements of the organizational context cornerstone of the PARIHS framework, and also whether other factors in the organizational context were perceived to influence KT in a specific low-income setting.Methods: This qualitative study was conducted in a district of Uganda, where focus group discussions and semi-structured interviews were conducted with midwives (n = 18) and managers (n = 5) within the catchment area of the general hospital. The interview guide was developed based on the context sub-elements in the PARIHS framework (receptive context, culture, leadership, and evaluation). Interviews were transcribed verbatim, followed by directed content analysis of the data.Results: The sub-elements of organizational context in the PARIHS framework-i.e., receptive context, culture, leadership, and evaluation-also appear to be relevant in a low-income setting like Uganda, but there are additional factors to consider. Access to resources, commitment and informal payment, and community involvement were all perceived to play important roles for successful KT.Conclusions: In further development of the context assessment tool, assessing factors for successful implementation of evidence in low-income settings-resources, community involvement, and commitment and informal payment-should be considered for inclusion. For low-income settings, resources are of significant importance, and might be considered as a separate sub-element of the PARIHS framework as a whole. © 2012 Bergström et al.; licensee BioMed Central Ltd. This study was carried out in a district of Uganda with about 20 health centers providing delivery services, including one general hospital with a bed capacity of about 100. The hospital has a catchment area beyond the district limits, and serves about 1.5 million individuals. The majority of people in the district earn their livelihood through farming. The study was conducted within a larger study with the aim to develop a quantitative assessment tool regarding context in low- and middle-income settings. The larger study is conducted within the Research for Improved Child Health network, and efforts similar to the study reported here are undertaken in Vietnam and Bangladesh; findings from those studies will be reported elsewhere. This study was carried out in a district where efforts to improve neonatal health and survival was ongoing, subjecting health workers, primarily midwives, and managers to change. A semi-structured guide was developed based on the four sub-elements of the context cornerstone (receptive context, culture, leadership, and evaluation) as suggested in the PARIHS framework (Figure 1) and inspired by the dimensions within its three developed tools [22,31-33]. Focus group discussions (FGDs) and individual interviews were conducted with midwives working in different levels of the healthcare services in the district in 2010. Individual interviews were also conducted with managers, for example, those in charge of health centers and health service managers at district level. All FGDs and individual interviews were conducted outside the respondent’s place of work to ensure confidentiality and allow for an open discussion. FGDs are considered a useful method for exploring new areas, because the interaction among group members brings out different opinions about the topic under discussion [34]. It has also been suggested that FGDs are a good data collection technique when discussing sensitive topics [35]. In this study, the FGDs served well for exploring prevailing perceptions about organizational context among midwives working at different health centers. However, they were less helpful when conducted with midwives working within the same unit, because it was challenging for participants to discuss leadership. Therefore, we conducted individual interviews with midwives working in the same unit. During the FGDs and interviews, the interviewers tried to clarify unclear concepts, and summarized the respondents’ statements to ensure clarity. To ensure credibility of our study, we triangulated methods as described above. Triangulation of methods allowed for the exploration of different aspects of the study objectives. Respondents were provided with reimbursement for their transportation costs. Following a pilot FGD with Ugandan midwives, to ensure comprehensiveness of the guide, the guide was used in both FGDs and individual interviews (Additional file 1). At the beginning of each session, respondents were asked to think of and briefly describe how the introduction of new knowledge and change in practice had occurred in their place of work, and throughout the session try to attach their perceptions of the relevance of the organizational context to those changes. In relation to the ongoing intervention to improve neonatal health and survival, several such changes were brought up during discussions, for example, neonatal resuscitation according to guidelines, the utilization of incubators, and the introduction of death review meetings. Data collection sessions were conducted in English (Uganda’s official language) and audio-recorded. Sessions lasted 45–110 minutes and were performed by AB and SN. After each data collection session, AB and SN discussed what had emerged, whether any changes should be made to the guide, and whether further probes were needed. We conducted two FGDs and a total of 10 individual interviews. All respondents were given written information about the study and agreed to participate. Two FGDs were conducted: one with six midwives from community health centers and one with midwives working in the hospital. Sampling for the first FGDs was purposive, whereby respondents from different parts of the district, working under different conditions in terms of distance to the district hospital and number of healthcare workers in the unit, were included. The second FGD included seven conveniently sampled midwives working in the antenatal clinic at the hospital. The reason for choosing this division was that the organizational context differed between the primary healthcare units and the district hospital. Because some aspects of the interview guide, primarily leadership, were difficult to discuss during the FGDs, the study team opted to continue data collection by conducting individual interviews with other midwives working in the same unit. Sampling for individual interviews with midwives and managers employed a purposive snowballing method [36]. In total, 23 (22 female, 1 male) individuals participated in the study; the mean age was 39 years (range, 26–55 years), the median years since qualification was eight (range, 2–34), and the median number of years they had worked in the present place of work was four (range, 1–30 years) (Table 1). The reason for inviting midwives and managers involved in the provision of maternal and neonatal health and survival was the fact that there was an ongoing intervention study in the district from which participants could draw experiences. Description of participants Preliminary analysis and discussions were held directly after each FGD and interview to agree on the level of saturation, that is, when the researcher is no longer hearing new information and ends data collection. The audio-recorded data were transcribed verbatim by AB and imported to QSR NVivo 8 software, followed by primarily using directed content analysis as suggested by Hsieh and Shannon [37]. The goal of a directed content analysis is to validate or conceptually extend a theoretical framework or theory [37]. This deductive directed approach implied a more structured process compared with inductive content analysis. Using prior research and existing theory, in this case the PARIHS framework and publications relating to it [17,18,20-22,25], a thorough reading of the transcripts was followed by identifying and highlighting key concepts that represented the four sub-elements in the semi-structured guide. Next, all highlighted passages were coded. Further reading, and employing an inductive approach, as suggested by Graneheim and Lundman [38], led to the identification of additional factors perceived to impact upon the implementation process, which could not be categorized within the initial scheme. AB performed the analysis and findings were then discussed in the research group to reach consensus with regard to what they reflected. Examples of the analysis process are presented in Tables 2 and ​and3.3. In addition, we discussed our findings with peer de-briefers to provide a fresh perspective for analysis and critique [39]. In this study, peer de-briefers included two health practitioners and public health researchers from low-income settings and one Swedish implementation researcher. In total, we involved three peer de-briefers to question the findings from their separate perspectives. Example of the qualitative directed content analysis process Example of the qualitative inductive analysis process Ethical approval was obtained from the Makerere University School of Public Health Review Board and the Uganda National Council of Science and Technology. All respondents were given written information about the study prior to participation and written consent was obtained. Voluntary participation and confidentiality were ensured, and respondents were informed of their right to withdraw from the study at any time. They were also told that data would be analyzed after being de-identified. Data collection was undertaken outside of respondents’ working units to ensure confidentiality and avoid disturbance.