part 2 .docx

Week 10 - Qualitative Data Analysis  What are Data? “A representation of facts or concepts in an organized manner in order that it may be stored, communicated, interpreted, or processed...” “Information collected by a researcher; an organised information. ...often thought of as statistical or quant...

Week 10 - Qualitative Data Analysis  What are Data? “A representation of facts or concepts in an organized manner in order that it may be stored, communicated, interpreted, or processed...” “Information collected by a researcher; an organised information. ...often thought of as statistical or quantitative, but they may take other forms as well-such as transcripts of interviews or videotapes of social interactions. Different Sources of Qualitative Data  Many Reasons Qualitative Approaches are Used  Qualitative approaches are often used to address broader research questions, rather than testing very specific hypotheses Qualitative approaches can be used for different reasons Theory development Theory testing, Description, Comparison Instrument development Purpose of your research will determine details of the way you do your analysis E.g., ethnographic studies or grounded theory approaches may be guided by different principles and lenses, may involve farmore data, several more iterations between analysis and data collection and less structure in the analysis than case studies Degree of structure in qualitative analysis  Research Question: What factors influence learning from errors: Phase 1 - Qualitative study objective  Use focus groups and expert panel to validate typologies of  Error and AEs  Ways units and orgs learn  Both from the perspective of providers and managers  Overlap in data collection and analysis Data analysis beings during data collection  Careful note taking central to good data collection  Include your reflections as your record…continue to include your reflections (memos) as you go thru the steps of analysis  Add comments on non-verbal communication, setting, circumstances  Common Features of Qualitative Analysis: Giving codes to initial materials collected (interviews, docs, etc) Adding comments/reflections  Combining materials for similar themes, relationships, group differences or whatever your focus  Taking the themes that emerge out to the field to structure next wave of data collection  Distil a few generalizations that reflect clear consistencies in your data  Link your generalizations back to existing literature and theory  The case of the KT forums shown all but #4 Data Reduction as a goal of Qualitative Analysis Data reduction is the process of selecting, focusing, simplifying, abstracting, coding and transforming the raw data into information or themes that allow you to tell a story  To code or not to code? Analytical choices, biases, expectations will shape the story your data tells  Data reduction DOES NOT reduce everything to numbers  Must keep the context to retain the meaning…use quotations to illustrate your themes  The Analysis Process:Pre-Analysis: Conversion of Raw Data Most sources of qualitative data are converted to TEXT, including field notes, tape recorded, interviews, video taped activities, etc  Transcription: interview time = 5:1  Qualitative data management software provides you a way to organize, retrieve, to manage- your data- it does not analyze data for you  Nvivo is the most widely used qualitative software package  Steps in Data Analysis:  Step 1: Get a sense of the whole Read through the data sets, or at least the first few interviews Write down broad ideas as you bread  If you moderated, you will already have a good sense of this  Step 2: Generate topics, if you had specific questions you were looking for answers for, list those as topics Reading through again, ask yourself What is this about? Is it important?  Note topics and themes you think you see emerging under each topic  Question 1:  What kinds of events might provide valuable opportunities for learning and reducing similar events in the future? Events can be differentiated based on several things: Whether they are preventable  How serious the outcome is  How often the event occurs  When the event is discovered  Where the event occurs  Question 2- 5 categories of PLEs Near miss Preventable AE causing minimal harm but no prolonged hospitalization  PAEs that results in prolonged hospitilzation but no disability at discharge  PAEs that results in disability at discharge  PAEs that results in death  Question 3: What happens after an event? Are things done that help you learn from these events?  Examples of responses/things to promote learning following an event: Incidents are submitted to an incident reporting system that the hospital uses  Investigations are carried out which ask specifically about factors contributing to the incident and corrective strategies are suggested  Incident data are reviewed and aggregate by unit, dept, or other meaningful grouping and are fed back to reporting units The most appropriate provider discloses information about the incident to the patient and or family Focus Group analysis - topics Topic 1- the value of different kinds of events for learning  Topic 2 - the extent to which providers and managers are able to think in terms of the 5 categories adapted from incidence studies  Topic 3 - the different ways units and organizations lean in response to events  Step 3: Develop themes more thoroughly Make a more complete list of topics and their themes  For each topic and theme create label that best represents its meaning  Topic 1: Themes  Preventable events  Near misses  Frequent events  Events with severe outcomes  Hodgepodge (medication errors, falls) Step 4: Try out classification scheme  Go back to a transcript and code the data using your topics and themes  Step 5: Refine themes  Do they fit the data  How many topics are necessary  What about leftovers Relevant or irrelevant  Repeat step 4  Keep qualitative nature and richness of data  Step 6: Identifying patterns and themes  Patters are the relationships among categories  We are starting to tell our story  Pre determined or developed an ongoing basis  Usually a combination of both  This can be the basis of theory development  Step 7: Drawing and verifying conclusion  Continuous process, based on  Summary of data  Identifying trends  Identifying associations - local causation  Consider counterarguments  Step 8: Strategies to confirm findings  Consider representativeness of your themes - what have you chosen from the transcripts  Observers bias  Take your themes back out to the filed for verification All the steps in data analysis:  Step 1 - get a sense of the whole  Step 2 - generate / list topics  Step 3 - develop themes  Step 4 - try out classification scheme  Step 5 - refine themes  Step 6 - identifying patterns and them  Step 7- drawing and verifying conclusions  Step 8 - strategies to confirm findings  Week 11- Knowledge Translation  Last weeks findings:  Who might be interested within these findings:  Policy makers would be interested  Hospital stakeholders (vice presidents, presidents, ceos, board members) Front line providers (Doctors, physicians, nurses)  - maintain the quality of care on the floor  How do we get the results to these folks: Publish on academic journal  Other researchers look at these results to compare  Does everyone (within the hospital setting) need the same info, level of detail: No they dont  Researchers - look at previous theories and methods  Ceo, nurse, managers etc- what does this study mean, look at the abstract  What is Knowledge Translation: The exchange, synthesis and ethically- sound application of research findings within a complex set of interactions among researchers and knowledge users Knowledge translation can be seen as acceleration of the knowledge cycle; an acceleration of the natural transformation of knowledge into users  It’s about using research evidence in practice  CIHR (2004) definition  Getting research findings used in practice to make a change  Direct and indirect use are valuable  Symbolic is problematic  Why the Emphasis on KT?: How are research results traditionally shared for use?  What's wrong with this process?...Why aren't research results (multiple studies that are synthesized into evidence) used?  Some of the most straightforward evidence is not used: Using clinical research in practice  Research has clearly shown the benefits of using aspirin and beta blockers following an MI and in patients with coronary heart disease  Only, 76% of patients with an acute MI who should receive this therapy (those without contraindication to aspirin therapy) receive such therapy at discharge  Data published by the National Cooperative Cardiovascular Project have shown that fewer than half of all post-MI patients receive beta-blockers as long term therapy  Suggests a massive underutilization or research findings  Using clinical research in practice: Found that 30% of patient who underwent angioplasty had a contraindication for the procedure, as determined by Amer College of Cardiology guidelines (Leape et al 2003) This means there is 30% of cases they studied where angioplasty was used, there was a general agreement that it wasn't useful or effective and may have been harmful  Gaos in use of this kind of clinical evidence is striking given its inherent usability compared to other research evidence  Evidence Based Medicine (EBM): EBM emerged in response to showing huge (unjustified) practice variation in the 70s  EMB is the “conscientious, explicitly, and judicious use of the current best evidence in making decisions about the care of individual patients) Ignore KT; drug companies spent 10x what research agencies on dissemination  The Knowing doing app gap = how long it takes from the knowledge we know to putting it into action takes roughly 20 years  Use of clinical research is similar to knowledge use  KT on the Clinical SIde:  Efforts to translate clinical research into practice guidelines to make it easier to apply research evidence  Translation of clinical research into CPG is an example of KT  Evidence is incorporated into flowcharts to make a influence  Model of how knowledge is Used:  Landry et all describes 4 models: Science push model  Science are the source of ideas  Useful application will derive from good science Flaws: No one responsible for transfer  Raw research info is not “usable” by most  Demand Pull model Users become the main source of ideas for directing research customer/contactor relationship  Useful knowledge is produced by giving constraints to scientists practitioners/decision makers should tell scientists what the problem is  Flaws: Too much emphasis on users, not on good research  Timelines of researchers and dms font match anyway  Dissemination model Addsa a step to the research process that includes dissemination mechanisms to transfer knowledge to potential users  Dissem models. Potential users not involved int he research of the selection of info for transfer Flaws: Reception of results does not mean it gets used  Reinforces the “2 communities” problems  Interaction model Knowledge is produced and co-interpreted by scientists and practitioner and high levels of interaction should be sought  Argues that KU depends on sustained interaction an the more sustained and intense the interactions the more likely the utilizatio  Greater attention to the relationship between researchers and users than to the nature and quality of the research  Most challenging model for scientist and practitioners For buy in, support and interest  Flaw:  Negotiations of process, politics comes into research  Focus on science push model and interaction model not demand pull model and dissemination model  Mode 1 - like the science push model  traditional mode of organizing universities and research. main objective is the production of new knowledge research structured in established disciplinary boundaries scientific achievement within these boundaries scientists are the guardians of quality knowledge diffusion via publication in peer-reviewed journals application derives from this process but is not predominant goal funding research is a public responsibility universities are the main loci Mode 2 - like the interaction model  Problem-solving is the main objective including developing capability in society Heterogeneous team + unstable social structure Transdisciplinariy Contextualization of research and the localization of research End of academic monopoly on assessment of research (ex.: Merit panel at CHSRF) Diversification and de-institutionalization of knowledge diffusion activities  ~ = Interaction Model Terminology  As models have evolved - so has the process of terminology  What started as Research Utilization is now = Knowledge Exchange in healthcare  Why bring decision makers into the research process? What works?  “Bringing decision makers who can use the results of a particular piece of research into its [the research’s] formulation and conduct is the best predictor for seeing the findings applied.” (Lomas, 2000a: 237) “the clearest message from evaluation of successful research utilization is that early and ongoing involvement of relevant decision makers in the conceptualization and conduct of a study is the best predictor of its utilization” (Lomas, 2000b:141, emphasis mine). Decisions makers need to be used within early involvement  The Dissemination Effort Model Key interaction model  Humberman argues that “interpersonal links, spead through the life of a given study, are the key research use  Parsimony may not be a strength of Humbermans model, comprehensiveness is  Why is interaction so important? KU us a political process  Consider results on benefits of privatization or societal impact of liberalizing abortion laws  1 recent study found that interaction did not influence utilization of study results because study findings were in conflict with decision makers organizational and political interests  KU involves cognitive processes susceptible to bias  Info or research findings that are consistent with our expectations tends to be accepted while info that is inconsistent tends to be challenged, questioned and ultimately disregarded  Research findings that confirm existing public health practices on breast cancer prevention approaches were used equally by groups who interact with the researchers and those that did not  KU is a social process  RU and KT are highly social processes that are more successful in the presence of positive social interaction between communities  Relationships and face to face contact are more effective  Why is it Important:  KU is political and susceptible to cognittive biases, the social piece that is central to interaction becomes critical  Using face to face contact  But our implementation science paper asked if that interaction model goes too far in suggesting that early and sustained interaction is always warranted  Results and Interpretation: The results suggests that, of 2.5 million admissions to acute care hospitals in Canada in 2000 between 9,250 to 23,750 deaths occured due to Adverse Events that were preventable  Interpretation  Conclusions Sensitively delivered training initiatives for nurse leaders can help to foster a safety culture Organizational leadership support for improvement is however, also critical for fostering a culture of safety Together, training interventions and leadership support may have the most significant impact on patients safety culture  What kind of interaction might be helpful for the safety culture study? Early to help define the intervention  What kind of interaction might be most appropriate for the CAES? Policy makers, people providing care in hospital, all stakeholders in healthcare would be interested in this intervention  Later to interpret and plan for improvement  The case of the NFAEs and After Healthcare Now What we found studying the NFAEs  Indirect “conceptual” use of the CAES study info  Its political- the majority of interest and attention was focused on dealing with media  DMs need help and direction with data  DMs operate on shorter “just in time” horizons than researcher  Early and sustained interaction can be expensive and onerous  Might there be some cases where decision maker involvement is most appropriate only at the interpretation and dissemination stage  Conditions for Later Interaction  Studies with multiple, diverse stakeholder  Multiple stakeholders may have competing or inconsistent priorities which may complicate an already challenging exchange process  Fixed methods (eg replication studies) Leave no room for decision maker input into early aspects of research  Highly public or political research Likely to attract interest regardless of prior involvement in the process  Addressability/actionability of results  Sometimes VERY RARE that there is no interaction  Discovered during the research that there is a very high risk and had to immediately disseminate it  What is Community Based Research: Community situated: begins with a research topic of practical relevance to the community (as opposed to individual scholars) and is carried out in community settings Collaborative: community members and researchers equitably share control of the research agenda through active and reciprocal involvement in the research design, implementation and dissemination Action-oriented: the process and results are useful to community members in making positive social change and to promote social equity Often uses a participatory action approach to research and program evaluation Major Barrier to KU In the face of research evidence, pervasive barriers to change exist in the contexts of clinical, policy, and organizational arenas The barriers, while different in each of these arenas, tend to include: –traditions of autonomy –politics and values –competing priorities –perverse incentives (MM) –lack of knowledge of what and how to change Spreading Knowledge - that models that may be useful  Where connections or relationships with opinion leaders are well developed, innovations spread more efficiently ✔  Social network analysis Social network analysis explores patterns of interaction among individuals and organizations empirically and across many organizations Networks in which knowledge is transferred readily can diffuse innovations to members broadly and rapidly Advice seeking networks, a specific type of social network, are especially actionable for spreading evidence-based innovations; they provide empirical evidence of who is influenced by whom for particular types of innovations Social network analysis identifies influential individuals and sites to target first in future interventions.  Connected to a lot of people  ✔ Diffusion of innovation Roger 1962 found: Diffusion of innovations of many types have been studied Diffusion can take a long time, especially if left to passive processes Orgs learn about an innovation, perceive pros and cons, decide to adopt, adapt it to suit their organization, consider how to implement Initial learning about an innovation usually comes through impersonal communication channels – the decision to adopt is largely determined by discovering what credible others have done and what they think about the innovation in question Rogers’ representation of the classical diffusion model considers diffusion as the process by which new ideas spread via communication channels over time among members of a social system