NLM Informationist Grant – Web Assisted Tobacco Intervention for Community College Students

Hasman, L; Berryman, Donna; McIntosh, Scott

doi:10.7191/jeslib.2013.1034

Cited by 8 publications

(14 citation statements)

References 5 publications

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“…Health sciences librarians are ideal partners for offering scientists at their institutions a range of data management services" (p. 1). Data services provided by health science libraries may vary from creating "a comprehensive data dictionary" and "a standardized data request form" (Gore, 2013, p. 22) to assign metadata to incoming data, naming and labeling research variables, and finding a data repository (Hasman, Berryman, & Mcintosh, 2013). As reported in by Hanson et al (2013), librarians are now providing "the deepest and most detailed data management support" to scientific research teams "to date" (p. 25).…”

Section: Introductionmentioning

confidence: 99%

Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

Tang

2019

Data and Information Management

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This paper reports the results of an international survey on research data management (RDM) services in libraries. More than 240 practicing librarians responded to the survey and outlined their roles and levels of preparedness in providing RDM services, challenges their libraries face, and knowledge and skills that they deemed essential to advance the RDM practice. Findings of the study revealed not only a number of location and organizational differences in RDM services and tools provided but also the impact of the level of preparedness and degree of development in RDM roles on the types of RDM services provided. Respondents’ perceptions on both the current challenges and future roles of RDM services were also examined. With a majority of the respondents recognizing the importance of RDM and hoping to receive more training while expressing concerns of lack of bandwidth or capacity in this area, it is clear that, in order to grow RDM services, institutional commitment to resources and training opportunities is crucial. As an emergent profession, data librarians need to be nurtured, mentored, and further trained. The study makes a case for developing a global community of practice where data librarians work together, exchange information, help one another grow, and strive to advance RDM practice around the world.

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Section: Introductionmentioning

confidence: 99%

Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

Tang

2019

Data and Information Management

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“…Further, as the current sample reflected smokers already motivated to quit, it is possible that personal smokefree policies would be even lower among CC students not motivated to quit. The combination of higher tobacco use rates in CC students (Berg, An, et al, 2011; CDC, 2016; Lenk, et al, 2012; Sanem, et al, 2009; VanKim, et al, 2012) and the current findings of markedly lower personal smokefree policies support the call for a focus on CC students as a priority population for intervention (Berg, An, et al, 2011; Hasman, et al, 2013; Prokhorov, et al, 2008). …”

Section: Discussionmentioning

confidence: 68%

Smoke-Free Home and Vehicle Policies Among Community College Smokers

et al. 2017

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Background Personal smokefree policies (home and vehicle) reduce secondhand smoke exposure, improve health, and increase quitting among smokers. Overall, 83.0% and 78.1% of Americans report smokefree homes and vehicles, respectively. However, little is known about such policies among 2-year community college (CC) students, who represent a large, diverse population with higher smoking rates and less negative attitudes towards smoking than 4-year college students. Methods Prevalence of, and factors associated with, personal smokefree policies were examined for 2475 CC smokers enrolled in a national trial of web assisted tobacco intervention. Results Few students had smokefree home policies (20.7%), smokefree vehicles (17.0%), both smokefree home and vehicle policy (4.2%), or any policy (home or vehicle; 31.2%). In logistic regression models, having children was associated with a smokefree home or any policy but not with a smokefree vehicle, and among subjects who had children, only 20% reported a smokefree home, and only 15% had a smokefree vehicle. In addition, not living with other smokers, living with parents or roommates/siblings (vs. alone), smoking later than 30 minutes after awakening, believing that smoking affects the health of others, and confidence in quitting were associated with presence of a smokefree home or any policy; no variables were significantly associated with presence of a smokefree vehicle. Conclusions CC students represent a priority population for intervention regarding smokefree homes and vehicles. Such intervention can decrease exposure of others, including children, and potentially increase the likelihood of quitting in this high risk population.

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“…In addition to testing the content of survey instruments and their items, extra time will be needed for testing the use of novel mHealth technologies (both hardware and software), including formative and process evaluation feedback, a consideration which has been noted in other mHealth development initiatives [ 51 , 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although software programs and hardware devices were not prospectively compared and tested with our LMIC project partners, we employed lessons learned from our previous work regarding reach and engagement considerations of eHealth strategies [ 49 , 50 ], and from the formative evaluation phase of one of our randomized controlled trials (RCTs) [ 51 ]. In this trial, surveys were developed and pretested for implementation with US English speakers with low health literacy across a large geographic area.…”

Section: Methodsmentioning

confidence: 99%

Development and Implementation of Culturally Tailored Offline Mobile Health Surveys

McIntosh¹,

Pérez-Ramos²,

Demment³

et al. 2016

JMIR Public Health Surveill

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BackgroundIn low and middle income countries (LMICs), and other areas with low resources and unreliable access to the Internet, understanding the emerging best practices for the implementation of new mobile health (mHealth) technologies is needed for efficient and secure data management and for informing public health researchers. Innovations in mHealth technology can improve on previous methods, and dissemination of project development details and lessons learned during implementation are needed to provide lessons learned to stakeholders in both the United States and LMIC settings.ObjectiveThe aims of this paper are to share implementation strategies and lessons learned from the development and implementation stages of two survey research projects using offline mobile technology, and to inform and prepare public health researchers and practitioners to implement new mobile technologies in survey research projects in LMICs.MethodsIn 2015, two survey research projects were developed and piloted in Puerto Rico and pre-tested in Costa Rica to collect face-to-face data, get formative evaluation feedback, and to test the feasibility of an offline mobile data collection process. Fieldwork in each setting involved survey development, back translation with cultural tailoring, ethical review and approvals, data collector training, and piloting survey implementation on mobile tablets.ResultsCritical processes and workflows for survey research projects in low resource settings were identified and implemented. This included developing a secure mobile data platform tailored to each survey, establishing user accessibility, and training and eliciting feedback from data collectors and on-site LMIC project partners.ConclusionsFormative and process evaluation strategies are necessary and useful for the development and implementation of survey research projects using emerging mHealth technologies in LMICs and other low resource settings. Lessons learned include: (1) plan institutional review board (IRB) approvals in multiple countries carefully to allow for development, implementation, and feedback, (2) in addition to testing the content of survey instruments, allow time and consideration for testing the use of novel mHealth technology (hardware and software), (3) incorporate training for and feedback from project staff, LMIC partner staff, and research participants, and (4) change methods accordingly, including content, as mHealth technology usage influences and is influenced by the content and structure of the survey instrument. Lessons learned from early phases of LMIC research projects using emerging mHealth technologies are critical for informing subsequent research methods and study designs.

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NLM Informationist Grant – Web Assisted Tobacco Intervention for Community College Students

Cited by 8 publications

References 5 publications

Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice

Smoke-Free Home and Vehicle Policies Among Community College Smokers

Development and Implementation of Culturally Tailored Offline Mobile Health Surveys

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