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Wekerle CIHR Team - ISPCAN 2017 WG #CIHRTeamSV SoMe Experiment
- 1. DOES SOCIAL MEDIA IN VIOLENCE PREVENTION MAKE SENSE? Christine Wekerle, Negar Vakili, Sherry Stewart, Tara Black wekerc@mcmaster.ca @DrWekerle #CIHRTeamSV
- 2. SOCIAL MEDIA CAN PROVIDE FOCAL MESSAGE
- 3. THE ROLE OF SOCIAL MEDIA IN CHILD MALTREATMENT RESEARCH • In child welfare practice (e.g., communicating with youth) • Data collection • Recruitment • Administer survey • Data analysis (e.g., trends with hashtags) • Triangulation • Dissemination of results (Knowledge Mobilization) • To broader audience (e.g., public, academics, service users, policy makers) vs expensive, difficult to access peer reviewed literature
- 4. EFFECTIVENESS OF SOCIAL MEDIA FOR KNOWLEDGE MOBILIZATION (1) Many professionals (i.e., academics, youth-supporting organizations) use social media (Rolls et al., 2016) (2) Social media could be a tool to disseminate successful or promising e- intervention approaches (e.g., gaming-based, e-assessment), and this may support professionals as well as targeting adolescent health risk behavior (Fiellin et al., 2017; Montanaro et al., 2015; Salazar et al, 2014) (3) Social media may have impact to evidence-based/research knowledge uptake about youth violence, health and resilience related outcomes: - The Pew Research Center 2014 survey on adolescent use of social media in the US show 86% youth use SoMe (vs. Keller et al., 2014) - 59% of youth searches for internet-based information was on violence and personal safety (Skinner et al., 2003)
- 5. SOCIAL MEDIA AND CHILD MALTREATMENT RESEARCH • Strengths: • Low cost • Ease of implementation • Access to difficult to reach populations • Reduce gap between practice and research • Limitations: • May miss those who are not “techy” or older adults or those with accessibility issues • Changes in technology • Ethics: Confidentiality, Informed consent • Professional boundaries
- 6. SOCIAL MEDIA: TARGETTING INTER- DISCIPLINARY, MULTI-MODAL LEARNING • KMB “Products” – Evidence-Based & High Interest • Create TED Ed Lessons from your videos or any video you have access to • Standard format (1) video (2) quiz (3) publications (4) discussion chat • #CIHRTeamSV: • (1) CSA, Risk and Resilience in Youth Suicidality: • http://ed.ted.com/on/6nReRcN0 • (2) Adverse Childhood Events (ACEs) & Childhood Maltreatment: • http://ed.ted.com/on/iOyQVfhd • (3) The Resilience Journey: The value of Interpersonal resilience • http://ed.ted.com/on/uNQzmOkB #CIHRTeamSV www.in-car.ca
- 7. #CIHRTEAMSV TWITTER EXPERIMENT • Research Question: Is a team-based social media strategy via Twitter effective for increasing research publication knowledge dissemination (Researchgate views) and knowledge uptake (Researchgate citations) ? • All Scientists and Trainees have Twitter accounts linked together and to the Team accounts (Team members “follow” each other on Twitter) • Minimum commitment of 1 Tweet/week during “Twitter On” • ABAB Experimental Design • December 2016 (baseline) – January 2017 (Twitter ”on”) – February 2017 (Twitter ”off”) – March 2017 (Twitter “on”) – April 2017 (Twitter “off”) – May 2017 (Twitter “on”) – June 2017 (Twitter “off)
- 8. Note: **p<.01, *p<.05 (one-tailed tests); Short-term impact of Twitter Level of Engagement Indices. Weekly Tweets (F (1.365, 24.572) = 4.271, p = .038, ηp 2 = .192). 0 10 20 30 40 50 60 70 Weekly Tweets Twitter Significant Differences in # Tweets from Baseline to Twitter- On and Twitter-Off Periods Baseline Twitter On 1 Twitter Off 1 Twitter On 2 Twitter Off 2 Twitter On3 Twitter Off3 * * * ** ** *
- 9. Note: **p<.01, *p<.05 (one-tailed tests); Short-term impact of Twitter Level of Engagement Indices. Weekly Profile Views (F (1.213, 16.977) = 6.697, p = .015 , ηp 2 = .324). 0 5 10 15 20 25 30 35 40 Weekly Profile Views ResearchGate Profile Views Significant Differences from Baseline to Twitter On to Twitter Off Baseline Twitter On 1 Twitter Off 1 Twitter On 2 Twitter Off 2 Twitter On 3 Twitter Off 3 ** ** ** ** * **
- 10. 0 50 100 150 200 Weekly Reads ResearchGate Reads Significant Differences from Baseline to Twitter-On and Twitter-Off Baseline Twitter On 1 Twitter Off 1 Twitter On 2 Twitter Off 2 Twitter On 3 Twitter Off 3 Note: **p<.01, *p<.05 (one-tailed tests); Short-term impact of Twitter Level of Engagement Indices. Weekly Profile Views (F (1.543, 32.399) = 6.235, p = .009, ηp 2 = .229). ** *** ** ** *
- 11. 0 5 10 15 20 25 30 Weekly Citations ResearchGate Citations Significant Differences from Baseline to Twitter On to Twitter Off Baseline Twitter On 1 Twitter Off 1 Twitter On 2 Twitter Off 2 Twitter On 3 Twitter Off 3 Note: **p<.01, *p<.05 (one-tailed tests); Short-term impact of Twitter Level of Engagement Indices. Weekly Profile Views (F (2.025, 28.346) = 9.822, p = .001, ηp 2 = .412). ** ** ** ** ** **
- 12. #CIHRTEAMSV • (1) Twitter activity seems to significantly impact short-term Kmb in terms of Dissemination e.g., Views, Reads • (2) Positive trends for research Kmb in terms of Uptake e.g., citations • Citations may be a longer-term measure (i.e., extend the follow- up timeframe on analytics collection) • Merit further research into this science of Kmb research area
- 13. THANK YOU FOR YOUR ATTENTION! Questions or Comments?
- 14. REFERENCES • Fiellin LE, Hieftje KD, Pendergrass TM, Kyriakides TC, Duncan LR, Dziura JD, Sawyer BG, Mayes L, Crusto CA, Forsyth BW, Fiellin DA • Video Game Intervention for Sexual Risk Reduction in Minority Adolescents: Randomized Controlled Trial • J Med Internet Res 2017;19(9):e314 • URL: https://www.jmir.org/2017/9/e314 • DOI: 10.2196/jmir.8148 • PMID: 28923788
- 15. REFERENCES • Keller B, Labrique A, Jain KM, Pekosz A, Levine O • Mind the Gap: Social Media Engagement by Public Health Researchers • J Med Internet Res 2014;16(1):e8 • URL: http://www.jmir.org/2014/1/e8 • DOI: 10.2196/jmir.2982 • PMID: 24425670 • PMCID: 3906700
- 16. REFERENCES • Montanaro E, Fiellin LE, Fakhouri T, Kyriakides TC, Duncan LR • Using Videogame Apps to Assess Gains in Adolescents’ Substance Use Knowledge: New Opportunities for Evaluating Intervention Exposure and Content Mastery • J Med Internet Res 2015;17(10):e245 • URL: http://www.jmir.org/2015/10/e245 • DOI: 10.2196/jmir.4377 • PMID: 26510775 • PMCID: 4642786
- 17. REFERENCES • Rolls K, Hansen M, Jackson D, Elliott D • How Health Care Professionals Use Social Media to Create Virtual Communities: An Integrative Review • J Med Internet Res 2016;18(6):e166 • URL: http://www.jmir.org/2016/6/e166 • DOI: 10.2196/jmir.5312 • PMID: 27328967 • PMCID: 4933801
- 18. REFERENCES • Salazar LF, Vivolo-Kantor A, Hardin J, Berkowitz A • A Web-Based Sexual Violence Bystander Intervention for Male College Students: Randomized Controlled Trial • J Med Internet Res 2014;16(9):e203 • URL: http://www.jmir.org/2014/9/e203 • DOI: 10.2196/jmir.3426 • PMID: 25198417 • PMCID: 4180355
- 19. REFERENCES • Skinner H, Biscope S, Poland B, Goldberg E • How Adolescents Use Technology for Health Information: Implications for Health Professionals from Focus Group Studies • J Med Internet Res 2003;5(4):e32 • URL: http://www.jmir.org/2003/4/e32 • DOI: 10.2196/jmir.5.4.e32 • PMID: 14713660 • PMCID: PMC1550577
Hinweis der Redaktion
- Rolls and colleagues reviewed 72 studies of health care professionals’ use of Social media. SM studied included Listservs (n=22), Twitter (n=18), general social media (n=17), discussion forums (n=7), Web 2.0 (n=3), virtual community of practice (n=3), wiki (n=1), and Facebook (n=1). Emerging use virtual communities to share knowledge Fiellin did an RCT of a digital health intervention (a video game intervention) for HIV sexual risk reduction in minority adolescents. Though no difference in delaying intercourse, the intervention group had increased sexual knowledge. Montanaro explored the use of videogame apps to improve substance use knowledge in adolescents through data of an RCT. 3 and 6-month follow up data showed that more playing time was related to increased knowledge of substance use. Salazar did an RCT of a web-based sexual violence bystander intervention for male college students. 6 months later, the intervention group intervened more often than controls, and gained more knowledge about consent. Pew Centre: 7/10 use social media; 86% of 18-29 yrs But Keller et al., 2014: A total of 181 (19.8%) of 912 professor- and scientist-track faculty provided usable responses. The majority of respondents rarely used major social media platforms. Of these 181 respondents, 97 (53.6%) had used YouTube, 84 (46.4%) had used Facebook, 55 (30.4%) had read blogs, and 12 (6.6%) had used Twitter in the prior month.
- Manipulation Check Twitter Weekly Tweets. Figure 1 shows the number of weekly Tweets that participants reported in each specific period (Twitter on and Twitter off periods) on a weekly basis. As can be seen, the data conformed to the expected ABABABA pattern with the number of weekly tweets increasing during Twitter on periods and decreasing during Twitter off periods, respectively. The repeated measures ANOVA revealed a statistically significant within-subjects effect of time (F (1.365, 24.572) = 4.271, p = .038, ηp2 = .192). Post-hoc tests revealed that there were significant increases in weekly tweets with each Twitter On period (i.e., F (1, 18) = 6.345, p = 0.011 for baseline to Twitter On 1; F (1, 18) = 5.727, p = 0.014 for Twitter Off 1 to Twitter On 2; F (1, 18) = 7.129, p = 0.008 for Twitter Off 2 to Twitter On 3). In addition, there were significant decreases in weekly tweets with each Twitter Off period (i. e., F (1, 18) = 4.641, p = 0.0225 for Twitter On 1 to Twitter Off 1; F (1, 18) = 3.771, p = 0.034 for Twitter On 2 to Twitter Off 2; F (1, 18) = 8.840, p = 0.004 for Twitter On 3 to Twitter Off 3). This pattern of findings indicates a highly effective twitter manipulation.
- Research Gate Weekly Profile Views. Figure 2 shows the number of Research Gate profile views that participants reported in each specific period (Twitter on and Twitter off periods) on a weekly basis. As can be seen, the data conformed to the expected ABABABA pattern with the number of weekly profile views increasing during Twitter on periods and decreasing during Twitter off periods, respectively. The repeated measures ANOVA revealed a statistically significant within-subjects effect of time (F (1.213, 16.977) = 6.697, p = .015 , ηp2 = ..324). Post-hoc tests revealed that there were significant increases in weekly profile views with each Twitter On period (i.e., F (1, 14) = 13.400, p = .002 for Baseline to Twitter On 1; F (1, 14) = 11.785, p = 0.002 for Twitter Off 1 to Twitter On 2; F (1, 14) = 5.151, p = 0.02 for Twitter Off 2 to Twitter On 3). Additionally, there were significant decreases in weekly profile views with each Twitter Off period (i.e., F (1, 14) = 6.917, p = 0.01 for Twitter On 1 to Twitter Off 1; F (1, 14) = 9.784, p = 0.004 for Twitter on 2 to Twitter off 2; F (1, 14) = 6.857, p = 0.01 for Twitter On 3 to Twitter Off 3).
- Research Gate Weekly Reads. Figure 3 shows the number of Research Gate reads that participants reported in each specific period (Twitter on and Twitter off periods) on a weekly basis. As can be seen, the data conformed to the expected ABABABA pattern with the number of weekly reads increasing during Twitter on periods and decreasing during Twitter off periods, respectively. The repeated measures ANOVA revealed a statistically significant within-subjects effect of time (F (1.543, 32.399) = 6.235, p = .009, ηp2 = .229). Post-hoc tests revealed that there were significant increases in weekly reads with each Twitter On period (i.e., F (1, 21) = 6.629, p = .009 for Baseline to Twitter On 1; F (1, 21) = 10.312, p = 0.002 for Twitter Off 1 to Twitter On 2; F (1, 21) = 5.664, p = .01 for Twitter Off 2 to Twitter On 3). Similarly, there were significant decreases in weekly reads with each Twitter Off period (i.e., F (1, 21) = 4.136, p = 0.03 for Twitter On 1 to Twitter Off 1; F (1, 21) = 13.177, p = 0.001 for Twitter On 2 to Twitter Off 2; F (1, 21) = 6.105, p = 0.01 for Twitter On 3 to Twitter Off 3).
- Research Gate Weekly Citations. Figure 4 shows the number of Research Gate citations that participants reported in each specific period (Twitter On and Twitter Off periods) on a weekly basis. As can be seen, the data conformed to the expected ABABABA pattern with the number of weekly citations increasing during Twitter on periods and decreasing during Twitter off periods, respectively. The repeated measures ANOVA revealed a statistically significant within-subjects effect of time (F (2.025, 28.346) = 9.822, p = .001, ηp2 = .412). Post-hoc tests revealed that there were significant increases in weekly citations with each Twitter On period (i.e., F (1, 14) = 9.019, p = .005 for Baseline to Twitter On 1; F (1, 14) = 13.125, p = 0.002 for Twitter Off 1 to Twitter On 2; F (1, 14) = 9.629, p = .004 for Twitter Off 2 to Twitter On 3). Similarly, there were significant decreases in weekly citations with each Twitter Off period (i.e., F (1, 14) = 12.881, p = 0.002 for Twitter On 1 to Twitter Off 1; F (1, 14) = 10.149, p = 0.004 for Twitter On 2 to Twitter Off 2; F (1, 14) = 12.474, p = 0.002 for Twitter On 3 to Twitter Off 3).