Applied Health Researchers Research Articles

The Necessity of qualitative research for advancing oral health

Researchers are engaged with producing knowledge. Through this knowledge production, they make claims about the world. For applied health researchers, our knowledge production is both a scientific as well as a moral activity. Increasingly, oral health researchers are turning to qualitative research, a research approach that takes science and morality seriously. Qualitative research pushes researchers to think about the different worlds in which people live and work, and endeavours to generate data that reflect those worlds. This paper argues that humans are complex, and that qualitative approaches are necessary for understanding how we are all deeply embedded in historical, social, cultural and political contexts, and why this matters when thinking about oral health. This paper also dispels myths about the limitations of qualitative research and proposes future directions to improve the sophistication of qualitative oral health sciences.

Methods Content Analysis: A Role in Applied Health Research

Text data is highly information-rich and accessing this information would greatly benefit applied health researchers and decision makers. Text data can be viewed as both qualitative and quantitative data by the researcher. When both the quality and the quantity of the data can be informative, a rigorous mixed methods approach is necessary to make best use of available analysis techniques to yield high quality inferences. In this analytic essay, a sketch of a suggested mixed methods content analysis method is provided, combining the rich interpretive power of close readings of text data by researchers with the robust quantitative modelling via machine learning. This mixed methods content analysis method appears promising for applied health research.

Reflexive Thematic Analysis for Applied Qualitative Health Research

Thematic analysis is a widely cited method for analyzing qualitative data. As a team of graduate students, we sought to explore methods of data analysis that were grounded in qualitative philosophies and aligned with our orientation as applied health researchers. We identified reflexive thematic analysis, developed by Braun and Clarke, as an interpretive method firmly situated within a qualitative paradigm that would also have broad applicability within a range of qualitative health research designs. In this approach to analysis, the subjectivity of the researcher is recognized and viewed not as problematic but instead valued as integral to the analysis process. We therefore elected to explore reflexive thematic analysis, advance and apply our analytic skills in applied qualitative health research, and provide direction and technique for researchers interested in this method of analysis. In this paper, we describe how a multidisciplinary graduate student group of applied health researchers utilized Braun and Clarke’s approach to reflexive thematic analysis. Specifically, we explore and describe our team’s process of data analysis used to analyze focus group data from a study exploring postnatal care referral behavior by traditional birth attendants in Nigeria. This paper illustrates our experience in applying the six phases of reflexive thematic analysis as described by Braun and Clarke: (1) familiarizing oneself with the data, (2) generating codes, (3) constructing themes, (4) reviewing potential themes, (5) defining and naming themes, and (6) producing the report. We highlight our experiences through each phase, outline strategies to support analytic quality, and share practical activities to guide the use of reflexive thematic analysis within an applied health research context and when working within research teams.

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.

ObjectiveInformative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.Materials and MethodsA systematic literature search was conducted by 2 independent reviewers using prespecified keywords.ResultsThirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).DiscussionThis is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.ConclusionsA growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.

Narrating the nadir: examining personal and vicarious stories of cancer-related low points among survivors and romantic partners

Objective: To investigate low point autobiographical narratives among cancer survivors and romantic partners.Design: Cancer survivors (Study 1) and romantic partners (Study 2) narrated the low points of their cancer experiences. Partners also narrated stories of cancer survivors’ low points. Narratives were coded for their manifest content, as well as redemption (negative-to-positive arc), contamination (positive-to-negative arc), and tone (positive or negative valence).Main Outcome Measures: Self-reported measures of health and well-being (life satisfaction, cancer-related worry, impact of cancer).Results: The diagnosis moment was the most frequently recognised low point among survivors and partners. Survivors who narrated contaminated low points reported marginally less somatisation, salience of cancer recurrence, and that cancer had a marginally less positive impact, relative to survivors whose narratives did not contain contamination. Tone in partners’ low points predicted marginally less worry and more somatisation. The tone of their vicarious low points negatively correlated with anxiety.Conclusion: This research contributes to the growing body of work examining, and giving voice to, the experiences of cancer survivors and those close to them. As such, it informs applied health researchers of potentially challenging cancer-related experiences, and the way(s) in which the storying of these experiences align with psychological flourishing.

Can We Re-Imagine Research So It Is Timely, Relevant and Responsive? Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada: A Call to 'Re-Imagine' Research".

Partnerships between academic institutions and healthcare organisations have been proposed as an effective way to integrate academic research findings into changes in health policy and practice. Bowen and colleagues explore these partnerships from a different angle, analysing them in relation to the experiences of health system leaders. The authors made a call to re-imagine research, rethinking how we train applied health researchers, fund health research and evaluation and design studies and collaborations with the health sector. In this paper, I respond to this call by discussing three strategies we can use to make sure our research is timely, relevant and responsive to the needs and context of healthcare organisations: the widespread use of rapid research approaches, the integration of scoping stages in all studies, and the training of applied health researchers to work in the health system and develop collaborative relationships with staff.

Designing high-quality implementation research: development, application, feasibility and preliminary evaluation of the implementation science research development (ImpRes) tool and guide

BackgroundDesigning implementation research can be a complex and daunting task, especially for applied health researchers who have not received specialist training in implementation science. We developed the Implementation Science Research Development (ImpRes) tool and supplementary guide to address this challenge and provide researchers with a systematic approach to designing implementation research.MethodsA multi-method and multi-stage approach was employed. An international, multidisciplinary expert panel engaged in an iterative brainstorming and consensus-building process to generate core domains of the ImpRes tool, representing core implementation science principles and concepts that researchers should consider when designing implementation research. Simultaneously, an iterative process of reviewing the literature and expert input informed the development and content of the tool. Once consensus had been reached, specialist expert input was sought on involving and engaging patients/service users; and economic evaluation. ImpRes was then applied to 15 implementation and improvement science projects across the National Institute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London, a research organisation in London, UK. Researchers who applied the ImpRes tool completed an 11-item questionnaire evaluating its structure, content and usefulness.ResultsConsensus was reached on ten implementation science domains to be considered when designing implementation research. These include implementation theories, frameworks and models, determinants of implementation, implementation strategies, implementation outcomes and unintended consequences. Researchers who used the ImpRes tool found it useful for identifying project areas where implementation science is lacking (median 5/5, IQR 4–5) and for improving the quality of implementation research (median 4/5, IQR 4–5) and agreed that it contained the key components that should be considered when designing implementation research (median 4/5, IQR 4–4). Qualitative feedback from researchers who applied the ImpRes tool indicated that a supplementary guide was needed to facilitate use of the tool.ConclusionsWe have developed a feasible and acceptable tool, and supplementary guide, to facilitate consideration and incorporation of core principles and concepts of implementation science in applied health implementation research. Future research is needed to establish whether application of the tool and guide has an effect on the quality of implementation research.

Sample Size Calculation in Clinical Studies: Some Common Scenarios

Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible.

What makes stroke rehabilitation patients complex? Clinician perspectives and the role of discharge pressure.

BackgroundApproximately 80% of people who survive a stroke have on average five other conditions and a wide range of psychosocial issues. Attention to biopsychosocial issues has led to the identification of ‘complex patients’. No single definition of ‘patient complexity’ exists; therefore, applied health researchers seek to understand ‘patient complexity’ as it relates to a specific clinical context.ObjectiveTo understand how ‘patient complexity’ is conceptualized by clinicians, and to position the findings within the existing literature on patient complexity.MethodsA qualitative descriptive approach was utilized. Twenty-three stroke rehabilitation clinicians participated in four focus groups.ResultsFive elements of patient complexity were identified: medical/functional issues, social determinant factors, social/family support, personal characteristics, and health system factors. Using biopsychosocial factors to identify complexity results in all patients being complex; operationalization of the definition led to the identification of systemic elements. A disconnect between acute, inpatient rehabilitation and community services was identified as a trigger for increased complexity.ConclusionsPatient complexity is not a dichotomous state. If applying existing complexity definitions, all patients are complex. This study extends the understanding by suggesting a structural element of complexity from manageable to less manageable complexity based on ability to discharge.

A Tutorial of Survival Modeling to Capture Covariate Effect

Mixture modeling in general and expectation–maximization in particular are too cumbersome and confusing for applied health researchers. Consequently, the full potential of mixture modeling is not realized. To remedy the deficiency, this tutorial article is prepared. This article addresses important applied problems in survival analysis and handles them in deeper generality than the existing work, especially from the point of view of taking covariates into account. In specific, the article demonstrates the concepts, tools, and inferencial procedure of mixture modeling using head-and-neck cancer data and survival time after heart transplant surgery data.

To Boldly Go: A Partnership Enterprise to Produce Applied Health and Nursing Services Researchers in Canada

This paper describes the origins of the Regional Training Centres (RTCs) from the perspective of the Canadian Health Services Research Foundation (CHSRF), a national funder of applied health and nursing services research in Canada. The author details the contributions of CHSRF, Canadian Institutes of Health Research (CIHR) and Capacity for Applied and Developmental Research and Evaluation (CADRE) program, as well as an essential feature of the RTCs: their application of the linkage and exchange model (Lomas 2000). The discussion encompasses the RTC program requirements and selection process, as well as the fourth-year review, the aim of which was to assess the early results of the RTCs. The role that CHSRF plays in facilitating the national network of RTCs is highlighted. The author concludes with reflections on what has worked well, what might be done differently and advice to others interested in developing graduate education based on the linkage and exchange model.