Doctor of Philosophy

dissertationNarrative persuasion research has identified two promising features that could influence behavior: (a) whether the character lives or dies (narrative outcomes) and (b) whether the character overcomes key barriers (narrative barriers). The current study manipulated both narrative features in a human papillomavirus (HPV) vaccine intervention - delivered via an online panel study - targeted to young adult women aged 18 to 26 (N = 246). Participants were randomly assigned to a 2 (survival vs. death) 2 (social vs. structural barriers) between subjects experiment. Compared to death narratives, survival narratives increased narrative plausibility, consistency, and coverage, and yielded greater HPV vaccination self-efficacy and lower perceived barriers to action. Narrative features interacted, such that survival narratives featuring social barriers led to greater transportation into the story than other combinations. Moderated mediation analysis was employed to test 10 theoretically-derived mediators, including transportation, four factors of believability, perceived barriers, perceived benefits, risk susceptibility, risk severity, and self-efficacy. Two variables emerged as mediators of the narrative message-behavioral intention relationship: transportation and risk susceptibility. The results provide an important first step toward building a more comprehensive and integrated model of narrative persuasion processing. These findings also have practical applications for guiding narrative public health message design in cervical cancer prevention campaigns. Results also highlight the clinically significant impact that narrative-based interventions can serve toward lessening the incidence of cervical cancer through an increase in HPV vaccination for young women. Directions for future work in the development of narrative persuasion and cancer communication are discussed

The Landscape of Connected Cancer Symptom Management in Rural America: A Narrative Review of Opportunities for Launching Connected Health Interventions

Background: The 2016 President’s Cancer Panel called for projects focusing on improving cancer symptom management using connected health technologies (broadband and telecommunications). However, rural communities, like those in Appalachia, may experience a “double burden” of high cancer rates and lower rates of broadband access and adoption necessary for connected health solutions. Purpose: To better understand the current landscape of connected health in the management of cancer symptoms in rural America. Methods: A literature search was conducted using four academic databases (PubMed, CINAHL, MEDLINE, and PsycINFO) to locate articles published from 2010 to 2019 relevant to connected cancer symptom management in rural America. Text screening was conducted to identify relevant publications. Results: Among 17 reviewed studies, four were conducted using a randomized controlled trial; the remainder were formative in design or small pilot projects. Five studies engaged stakeholders from rural communities in designing solutions. Most commonly studied symptoms were psychological/emotional symptoms, followed by physical symptoms, particularly pain. Technologies used were primarily telephone-based; few were Internet-enabled video conferencing or web-based. Advanced mobile and Internet-based approaches were generally in the development phase. Overall, both rural patients and healthcare providers reported high acceptance, usage, and satisfaction of connected health technologies. Ten of the 17 studies reported improved symptom management outcomes. Methodological challenges that limited the interpretation of the findings were summarized. Implications: The review identified a need to engage rural stakeholders to develop and test connected cancer symptom management solutions that are based on advanced mobile and broadband Internet technologies

Making Headlines: An Analysis of US Government-Funded Cancer Research Mentioned In Online Media

Objective To characterise how online media coverage of journal articles on cancer funded by the US government varies by cancer type and stage of the cancer control continuum and to compare the disease prevalence rates with the amount of funded research published for each cancer type and with the amount of media attention each receives. Design A cross-sectional study. Setting The United States. Participants The subject of analysis was 11 436 journal articles on cancer funded by the US government published in 2016. These articles were identified via PubMed and characterised as receiving online media attention based on data provided by Altmetric. Results 16.8% (n=1925) of articles published on US government-funded research were covered in the media. Published journal articles addressed all common cancers. Frequency of journal articles differed substantially across the common cancers, with breast cancer (n=1284), lung cancer (n=630) and prostate cancer (n=586) being the subject of the most journal articles. Roughly one-fifth to one-fourth of journal articles within each cancer category received online media attention. Media mentions were disproportionate to actual burden of each cancer type (ie, incidence and mortality), with breast cancer articles receiving the most media mentions. Scientific articles also covered the stages of the cancer continuum to varying degrees. Across the 13 most common cancer types, 4.4% (n=206) of articles focused on prevention and control, 11.7% (n=550) on diagnosis and 10.7% (n=502) on therapy. Conclusions Findings revealed a mismatch between prevalent cancers and cancers highlighted in online media. Further, journal articles on cancer control and prevention received less media attention than other cancer continuum stages. Media mentions were not proportional to actual public cancer burden nor volume of scientific publications in each cancer category. Results highlight a need for continued research on the role of media, especially online media, in research dissemination. Objective To characterise how online media coverage of journal articles on cancer funded by the US government varies by cancer type and stage of the cancer control continuum and to compare the disease prevalence rates with the amount of funded research published for each cancer type and with the amount of media attention each receives. Design A cross-sectional study. Setting The United States. Participants The subject of analysis was 11 436 journal articles on cancer funded by the US government published in 2016. These articles were identified via PubMed and characterised as receiving online media attention based on data provided by Altmetric. Results 16.8% (n=1925) of articles published on US government-funded research were covered in the media. Published journal articles addressed all common cancers. Frequency of journal articles differed substantially across the common cancers, with breast cancer (n=1284), lung cancer (n=630) and prostate cancer (n=586) being the subject of the most journal articles. Roughly one-fifth to one-fourth of journal articles within each cancer category received online media attention. Media mentions were disproportionate to actual burden of each cancer type (ie, incidence and mortality), with breast cancer articles receiving the most media mentions. Scientific articles also covered the stages of the cancer continuum to varying degrees. Across the 13 most common cancer types, 4.4% (n=206) of articles focused on prevention and control, 11.7% (n=550) on diagnosis and 10.7% (n=502) on therapy. Conclusions Findings revealed a mismatch between prevalent cancers and cancers highlighted in online media. Further, journal articles on cancer control and prevention received less media attention than other cancer continuum stages. Media mentions were not proportional to actual public cancer burden nor volume of scientific publications in each cancer category. Results highlight a need for continued research on the role of media, especially online media, in research dissemination. &nbsp

Health e-mavens: identifying active online health information users

U radu su pojašnjene razne metode i tehnike modeliranja vremenskih nizova te zatim i prikaz prediktivnih modela i rezultata koje su svaka od njih ostvarile. Pri samom uvodu i upoznavanju vremenskih nizova važno je primijetiti stvari koje treba imati na umu pri modeliranju. Vremenski nizovi zahtijevaju dobro razumijevanje podataka koje promatramo te dekompoziciju dijelova koje ga čine. Vremenski nizovi su specifični zato što se većinom radi o jednoj promatranoj varijabli, koja je u prizmi određenog vremenskog intervala. Uz to traži i razumijevanje podataka kroz vrijeme, te dobru detekciju pojava kao što su sezonalnost i trend. Prikazani su postupci od samog početka izgradnje modela kao što su čišćenje podatka, testiranje stacionarnosti, vizualizacija i slično. Prošli smo metode eksponencijalnog izglađivanja, ARIMA modele te TensorFlow paket Structural Time Series. U radu je prikazano kako svaki od tih metoda može ostvariti jako dobre rezultate. TensorFlow STS ipak se razlikuje od ostalih metoda budući da se radi o novoj biblioteci koja objedinjuje tradicionalne statističke metode koje se vežu za vremenske nizove i razne algoritme i principe strojnog učenja. Isto tako uzima za perspektivu Bayesovo razmišljanje o ažuriranju dokaza s dolaskom novih informacija.The paper explains various methods and techniques of time series modeling and then the presentation of predictive ones models and results achieved by each of them. During the introduction and introduction to the time series it is important to note the things to keep in mind when modeling. Time series require good understanding the data we observe and decomposing the parts that make it up. Time series are specific because it is mostly a single observed variable, which is in the prism of a certain time intervals. In addition, it requires an understanding of the data over time, and good detection of phenomena such as seasonality and trend. Procedures from the very beginning of model construction such as data cleaning, testing are presented stationary, visualization and the like. We went through exponential smoothing methods, ARIMA models and TensorFlow package Structural Time Series. The paper shows how each of these methods can accomplish strongly good results. TensorFlow STS, however, differs from other methods since it is a new library which combines traditional statistical methods that are linked to time series and various algorithms and principles of machine learning. It also takes as a perspective Bayes' thinking of updating the evidence with with the arrival of new information

The Best of Both Worlds: Exploring Cross-Collaborative Community Engagement

Lauded as a rewarding pedagogical approach, community-engagement can be time-consuming, resource-intensive, and difficult for instructors to manage for effective stu-dent learning outcomes. Collaborative teaching can allows instructors working in the same classroom to draw from each others’ expertise and share resources. In this essay, we propose a fruitful approach that brings the benefits of collaborative teaching to communi-ty-engagement. Two instructors collaborated to facilitate a community-engaged food jus-tice blog, demonstrating the benefits of combining these modalities. In this essay, we re-view relevant literature on collaborative teaching and community-engagement, presenting cross-collaborative community engagement as an innovative model for collaboration be-tween instructors in separate courses, allowing instructors to maintain autonomy while working together toward engaged learning.This is an article from The Journal of Effective Teaching 15 92015): 87. Posted with permission.</p

What cancer research makes the news? A quantitative analysis of online news stories that mention cancer studies.

Journalists' health and science reporting aid the public's direct access to research through the inclusion of hyperlinks leading to original studies in peer-reviewed journals. While this effort supports the US-government mandate that research be made widely available, little is known about what research journalists share with the public. This cross-sectional exploratory study characterises US-government-funded research on cancer that appeared most frequently in news coverage and how that coverage varied by cancer type, disease incidence and mortality rates. The subject of analysis was 11436 research articles (published in 2016) on cancer funded by the US government and 642 news stories mentioning at least one of these articles. Based on Altmetric data, researchers identified articles via PubMed and characterised each based on the news media attention received online. Only 1.88% (n = 213) of research articles mentioning US government-funded cancer research included at least one mention in an online news publication. This is in contrast to previous research that found 16.8% (n = 1925) of articles received mention by online mass media publications. Of the 13 most common cancers in the US, 12 were the subject of at least one news mention; only urinary and bladder cancer received no mention. Traditional news sources included significantly more mentions of research on common cancers than digital native news sources. However, a general discrepancy exists between cancers prominent in news sources and those with the highest mortality rate. For instance, lung cancer accounted for the most deaths annually, while melanoma led to 56% less annual deaths; however, journalists cited research regarding these cancers nearly equally. Additionally, breast cancer received the greatest coverage per estimated annual death, while pancreatic cancer received the least coverage per death. Findings demonstrated a continued misalignment between prevalent cancers and cancers mentioned in online news media. Additionally, cancer control and prevention received less coverage from journalists than other cancer continuum stages, highlighting a continued underrepresentation of prevention-focused research. Results revealed a need for further scholarship regarding the role of journalists in research dissemination

Comparing Theories of Media Learning: Cognitive Mediation, Information Utility, and Knowledge Acquisition from Cancer News

Determining what factors predict media learning is an important avenue of research for the field of mass communication. The present study provides a comparative investigation of two models of media learning: the cognitive mediation model and the information utility model. Participants (N = 1,076) read a news article related to scientific discoveries relevant to cancer prevention and responded to all constructs of the two models. Recognition and comprehension were used to measure knowledge acquisition. Results generally support previous predictions of each model, though predicted variance remains small. In addition to testing the existing models, a modified cognitive mediation model using a key construct related to information utility-perceived relevance-was tested. The refined cognitive mediation model offered a more nuanced understanding of certain causal mechanisms but did not result in a meaningful change in predictive power of the model. Implications of the theoretical comparison and integration are discussed

Health information seeking and scanning among US adults aged 50–75 years: Testing a key postulate of the information overload model

Past research has found that older US adults (aged 50–75 years) exhibit high levels of cancer information overload and cancer worry; however, no study to date has examined whether these perceptions are related to information seeking/scanning. To explore this relationship, older adults (N = 209, Mage = 55.56, SD = 4.24) were recruited to complete a survey measuring seeking, scanning, cancer information overload, and cancer worry. Most participants were high-scan/seekers (40.2%) followed by low-scan/seekers (21.1%), high-scan/no seekers (19.6%), and low-scan/no seekers (19.1%). Low-scan/no seekers had significantly higher cancer information overload compared to all other groups, consistent with the postulate that overload and seeking/scanning are negatively related. Low-scan/no seekers and high-scan/seekers both exhibited higher cancer worry severity, consistent with past research suggesting that cancer worry explains high levels of activity/inactivity