1) An annual survey of over 1,500 youth found that approximately one-third reported being victims of internet harassment in the form of rude comments or rumors being spread about them online.
2) Youth who were victims of traditional bullying, spent more time online, were older, and came from wealthier families were more likely to be victims of internet harassment.
3) Approximately one-fifth of youth reported perpetrating internet harassment against others, mainly through rude comments or spreading rumors.
4) Youth who were traditional bullies, victims of internet harassment themselves, used alcohol, and had tendencies toward anger were more likely to perpetrate internet harassment.
Colorado DHSEM: Understanding Social Media and Using it to Your AdvantageTrost, Micki
This presentation was delivered by the DHSEM Communications Specialist at the 2014 Colorado Safe Schools Summit. It discussing using social media to response and monitor in the school setting.
The Social Habit 2011 is a new study from Edison Research and Arbitron that looks at America's usage of Facebook, Twitter, Foursquare and other social networking sites and services. New representative and projectable statistics on the familiarity, usage and consumer behaviors associated with these sites and services is revealed, along with new data on brand following behavior and social commerce. This study was first presented at Blogworld East in New York in May 2011
Companies can learn the statistics and strategies to manage the fastest growing addiction killing corporate productivity: Internet compulsion/addiction.
Young Canadians in a Wired World – Phase III: Findings from Canada’s largest research project on children and teens’ Internet use are now available. Sexuality and Romantic Relationships in the Digital Age examines issues such as sexting, romantic interactions online and accessing pornography and information about sexuality.
Colorado DHSEM: Understanding Social Media and Using it to Your AdvantageTrost, Micki
This presentation was delivered by the DHSEM Communications Specialist at the 2014 Colorado Safe Schools Summit. It discussing using social media to response and monitor in the school setting.
The Social Habit 2011 is a new study from Edison Research and Arbitron that looks at America's usage of Facebook, Twitter, Foursquare and other social networking sites and services. New representative and projectable statistics on the familiarity, usage and consumer behaviors associated with these sites and services is revealed, along with new data on brand following behavior and social commerce. This study was first presented at Blogworld East in New York in May 2011
Companies can learn the statistics and strategies to manage the fastest growing addiction killing corporate productivity: Internet compulsion/addiction.
Young Canadians in a Wired World – Phase III: Findings from Canada’s largest research project on children and teens’ Internet use are now available. Sexuality and Romantic Relationships in the Digital Age examines issues such as sexting, romantic interactions online and accessing pornography and information about sexuality.
An updated look at the research and definitions around bullying and cyberbullying. Presented to the Youth Online Safety Working Group assembled by NCMEC, this talk unpacks both what current research can tell us about cyberbullying as well as where the gaps our understanding of this issue lie.
Amanda Lenhart delivered this presentation to the Year of the Child summit at the National Association of Attorneys General Year of the Child Conference, Philadelphia, PA, this talk surveys the current research on cyberbullying and online harassment, pulling in Pew Internet data as well as the work of the Crimes Against Children Research Center at the University of New Hampshire, Internet Solutions for Kids and other academics and scholars researching this topic. 5/13/09
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
1. NASPAG
Las Vegas, NV
8:15 am, April 15, 2010
Youth Internet Victimization:
Myths and Truths
Michele Ybarra MPH PhD
Center for Innovative Public Health Research
* Thank you for your interest in this
presentation. Please note that analyses included herein
are preliminary. More recent, finalized analyses may be
available by contacting CiPHR for further information.
2. Acknowledgements
This survey was supported by Cooperative Agreement number
U49/CE000206 from the Centers for Disease Control and
Prevention (CDC). The contents of this presentation are
solely the responsibility of the authors and do not necessarily
represent the official views of the CDC.
I would like to thank the entire Growing up with Media Study
team from Internet Solutions for Kids, Harris Interactive,
Johns Hopkins Bloomberg School of Public Health, and the
CDC, who contributed to the planning and implementation of
the study. Finally, we thank the families for their time and
willingness to participate in this study.
3. Technology use in the US:
Prevalence rates
More than 9 in 10 youth 12-17 use the
Internet (Lenhart, Arafeh, Smith, Rankin Macgill, 2008; USC Annenberg School
Center for the Digital Future, 2005).
71% of 12-17 year olds have a cell phone
(Lenhart, 4/10/2009) and 46% of 8-12 year olds have
a cell phone (Nielson, 9/10/2008)
4. Technology use in the US:
Benefits of technology
Access to health information:
About one in four adolescents have used the
Internet to look for health information in the
last year (Lenhart et al., 2001; Rideout et al., 2001; Ybarra & Suman, 2006).
41% of adolescents indicate having changed
their behavior because of information they
found online (Kaiser Family Foundation, 2002), and 14% have
sought healthcare services as a result (Rideout,
2001).
5. Technology use in the US:
Benefits of technology
Teaching healthy behaviors (as described by My
Thai, Lownestein, Ching, Rejeski, 2009)
Physical health: Dance Dance Revolution
Healthy behaviors: Sesame Street‟s Color
me Hungry (encourages eating vegetables)
Disease Management: Re-Mission (teaches
children with cancer about the disease)
6. Technology use in the US: risks
Behavior and psychosocial problems have been noted
concurrently for youth involved in Internet harassment
and unwanted sexual solicitation
Victims:
Interpersonal victimization / bullying offline (Ybarra, Mitchell, Espelage,
2007; Ybarra, Mitchell, Wolak, Finkelhor, 2006; Ybarra, 2004)
Alcohol use (Ybarra, Mitchell, Espelage, 2007)
Social problems (Ybarra, Mitchell, Wolak, Finkelhor, 2006)
Depressive symptomatology and suicidal ideation (Ybarra, 2004;
Mitchell, Finkelhor, Wolak, 2000; The Berkman Center for Internet & Society, 2008; Hinduja &
Patchin, in press)
School behavior problems (Ybarra, Diener-West, Leaf, 2007)
Poor caregiver-child relationships (Ybarra, Diener-West, Leaf, 2007)
8. Objectives
1. Have a working definition for internet harassment and
sexual solicitation
2. Be aware of the annual prevalence rate of youth
affected by internet victimization
3. Be able to identify the characteristics of youth more
likely to be victimized online
4. Understand the research supporting and refuting
assumptions about internet victimization.
9. Growing up with Media survey
Longitudinal design: Fielded 2006, 2007, 2008
Data collected online
National sample (United States)
Households randomly identified from the 4 million-
member Harris Poll OnLine (HPOL)
Sample selection was stratified based on youth age and
sex.
Data were weighted to match the US population of
adults with children between the ages of 10 and 15 years
and adjust for the propensity of adult to be online and in
the HPOL.
10. Eligibility criteria
Youth:
Between the ages of 10-15 years
Use the Internet at least once in the last 6 months
Live in the household at least 50% of the time
English speaking
Adult:
Be a member of the Harris Poll Online (HPOL) opt-in panel
Be a resident in the USA (HPOL has members internationally)
Be the most (or equally) knowledgeable of the youth‟s media use
in the home
English speaking
11. Youth Demographic Characteristics
2006 (n=1,577) 2007 (n=1189) 2008 (n=1149)
Female 50% 50% 51%
Age (SE) 12.6 (0.05) 13.7 (0.05) 14.5 (0.05)
Hispanic ethnicity 18% 17% 17%
Race: White 70% 72% 72%
Race: Black / African American 15% 13% 14%
Race: Mixed race 7% 9% 9%
Race: Other 8% 6% 6%
Household less than $35,000 25% 24% 25%
Internet use 1 hour+ per day 47% 49% 52%
13. Working Definition of
Internet harassment
As of yet, there is no generally agreed upon
definition for harassment.
In general, “Internet harassment” is obnoxious
behavior directed at someone with the intent to
harass or bother them. It:
Occurs online.
It can, but does not necessarily include text
messaging.
Can occur once or more often.
Can occur between people of equal power.
14. Working Definition of
Internet harassment
Harassment is different from bullying, which
(usually) is defined to be repetitive, over time,
between people of unequal strength.
Why does the lack of definition matter?
Different questions / behaviors queried
No distinction between „harassment‟ and „cyber
bullying‟
As a result, we have different prevalence rates
(6% - 72%)
15. Working definition of
Internet harassment
Does the lack of consensus mean all is lost?
No –
…As long as you understand the differences in
methodologies
Note too: because even with these differences,
we‟re seeing concurrence on psychosocial
„profiles‟ (more later..)
16. Involvement in Internet harassment
Not involved
62%
Victim-only
18%
Perpetrator-only
3%
Perpetrator-
victim
17%
Internet harassment
17. Annual prevalence rates of youth victims
of Internet harassment
Type (Monthly or more often) 2006 2007 2008
ANY 33% 8% 34% 9% 39% 9%
Someone made a rude or mean
comment to me online.
29% 7% 31% 8% 35% 8%
Someone spread rumors about me
online, whether they were true or not.
12% 2% 17% 3% 19% 3%
Someone made a threatening or
aggressive comment to me online.
14% 3% 14% 3% 15% 3%
Someone my age took me off their buddy list
because they were mad at me
26% 3% 30% 4%
Someone posted a picture or video of me in
an embarrassing situation
1.5% 0.7% 3% 0.6%
“Revised” total 41% 10% 45% 10%
19. Very / extremely upset by the
harassment – age constant (12-15 y.o.)
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Rude / mean comments Rumors Threatening /
aggressive comments
2006
2007
2008
20. Characteristics of youth more likely to be
victims of Internet harassment
Youth Characteristics OR (95% CI)
Demographic characteristics: Internet use 1 hour+ per day 2.1 (1.7, 2.7)
Age 1.2 (1.2, 1.3)
White (vs. non-White) 1.9 (1.4, 2.7)
Income: >$75,000 (vs. <$35,000) 1.5 (1.1, 2.2)
Psychosocial characteristics: Perpetrator: Internet harassment 8.6 (6.4, 11.6)
Perpetrator: Relational bullying 1.3 (1.0, 1.7)
Victim: Relational bullying 3.2 (2.4, 4.4)
Victim: Physical harassment 1.7 (1.2, 2.4)
School: Weapon past 30 days 3.7 (1.2, 11.6)
Caregiver: emotional closeness 1.1 (1.0, 1.2)
Above characteristics are in one multi-variate, population-average (GEE) model. Odds ratios are therefore adjusted for all other
characteristics shown, as well as the following characteristics, which were not statistically significant:: youth sex, Hispanic ethnicity,
alcohol use, marijuana use, poor grades in school, the number of suspensions or detentions in school, physical harassment
perpetration (offline), propensity to respond to stimuli with anger, caregiver monitoring, caregiver coercive discipline, and time
21. Annual prevalence rates of youth
perpetrators Internet harassment
Type (Monthly or more often) 2006 2007 2008
ANY 21% 4% 19% 3% 23% 4%
Made a rude or mean comment to
someone online.
18% 3% 17% 3% 21% 4%
Spread rumors about someone online,
whether they were true or not.
11% 2% 10% 0.7% 11% 0.7
Made a threatening or aggressive
comment to someone online.
5% 1.5% 5% 0.4% 8% 1%
Took someone your age off their buddy list
because I was mad at them
25% 3% 26% 2.3%
Posted a picture or video of someone in an
embarrassing situation
1% 0.6% 2% 0.3%
“Revised” total 31% 4% 35% 5%
23. Characteristics of youth more likely to be
perpetrators of Internet harassment
Youth Characteristics OR (95% CI)
Demographic characteristics: Internet use 1 hour+ per day 1.4 (1.0, 1.9)
Age 1.2 (1.1, 1.3)
Psychosocial characteristics: Victim: Internet harassment 10.0 (7.4, 13.6)
Perpetrator: Relational bullying 4.0 (3.0, 5.4)
Perpetrator: Physical harassment 1.9 (1.4, 2.7)
Alcohol use 1.6 (1.1, 2.4)
Propensity to respond to stimuli
with anger (STAXI)
1.1 (1.0, 1.1)
Time 2 vs. Time 1 0.7 (0.5, 0.99)
Above characteristics are in one multi-variate, population average (GEE) model. Odds ratios are therefore adjusted for all other
characteristics shown, as well as the following characteristics, which were not statistically significant:: youth sex, race, Hispanic
ethnicity, household income, marijuana use, poor grades in school, the number of suspensions or detentions in school, carrying a
weapon to school in the past 30 days, physical harassment and relational bully victimization (offline), caregiver monitoring,
emotional closeness with caregiver, and caregiver coercive discipline
24. Assumptions about Internet harassment
Everyone‟s doing it
It‟s increasing over time
It‟s getting nastier / kids are more affected
Everyone‟s a hapless victim
25. None of these assumptions are supported
by the data
“Everyone‟s doing it”:
38% (about 2 in 5) are involved in harassment
That means that 62% (3 in 5) are NOT involved in any way
It‟s increasing over time
Neither perpetration nor victimization rates appear to be increasing from
2006-2008
It‟s getting nastier / kids are more affected
There is no indication that young people are more likely to be upset by
harassment now (in 2008) then they were 2 years ago (2006). If anything,
there‟s some indication that youth are *less* likely to be upset now.
Everyone‟s a hapless victim
17% of all youth are BOTH victims and perpetrators of harassment
The odds of victimization increase about 8 fold if you are a perpetrator and
vice versa
26. Other things to note:
Few young people experience persistent
(monthly or more often) harassment
There seems to be strong overlap between
bullying offline and harassment online.
This is true for perpetration and for victimization
If you are involved online, you are likely involved offline
This is particularly true for relational harassment offline
Perhaps because online harassment is more amenable to
relational versus physical harassment / bullying behavior..?
27. Other things of note:
These kids are experiencing a multitude of
problems aside from being involved in
harassment:
Victims: bringing a weapon to school in the past 30
days; poor caregiver emotional relationship;
Perpetrators: alcohol use, anger management issues
Kids don‟t operate in a vacuum. If they‟re having
problems online, it‟s likely they‟re having
problems offline too.
29. Working definition of
cyberbullying
As of yet, there is no generally agreed upon
definition for cyberbullying
Some use Olweus‟ definition; other use a list of
definitions
We define it as:
Being online
Differential power
Repetitive
Over time
31. Overlap of cyberbullying and Internet
harassment victimization
Not involved
62%
Cyberbully-only
victim
1%
Internet
harassment-only
victim
24%
Cyberbully +
Internet
harassment
victim
13%
34. Characteristics of youth more likely to be
victims of cyberbullying
Youth Characteristics OR (95% CI)
Demographic characteristics: Internet use 1 hour+ per day 1.6 (1.0, 2.5)
White (vs. non-White) 1.8 (1.1, 3.0)
Psychosocial characteristics: Victim: Internet harassment 15.5 (8.5, 28.3)
Perpetrator: Internet harassment 2.2 (1.4, 3.3)
Victim: Physical harassment 2.7 (1.5, 4.7)
Above characteristics are in one multi-variate, population-average (GEE) model. Odds ratios are therefore
adjusted for all other characteristics shown, as well as the following characteristics, which were not statistically
significant: youth sex, age, race, Hispanic ethnicity, household income, alcohol use, marijuana use, poor grades in
school, the number of suspensions or detentions in school, carrying a weapon to school in the past 30 days,
caregiver monitoring, poor caregiver-child emotional relationship, caregiver coercive discipline, and time
35. Assumptions about cyberbullying
Cyberbullying is the same as Internet harassment
Cyberbullying is more common as Internet
harassment
Cyberbullying is more damaging than Internet
harassment
36. None of these assumptions are supported
by the data
Cyberbullying is the same as Internet harassment
If you accept that bullying must be: repetitive, over time, and between
two people with differential power; THEN any measure that does not
delineate this is not measuring cyberbullying
Due to a lack of consensus in measurement, this is not necessarily an
agreed-upon assertion however
Cyberbullying is more common than Internet harassment
On average (between 2007-2008): 37% were harassed, 14% were
bullied online in the past year
Cyberbullying is more damaging than Internet harassment
Among those cyberbullied, 15% report being very / extremely upset
Among those harassed, between 17-34% report being very /
extremely upset
38. Working Definition of
Unwanted Sexual Solicitation
Unwanted sexual solicitation was first studied by
Dr. David Finkelhor and colleagues at the
University of New Hampshire in response to
concerns from government and non-profit
agencies that youth were being “solicited” online
Like harassment, it:
Occurs online.
It can, but does not necessarily include text
messaging.
Can occur once or more often.
39. Working Definition of
Unwanted Sexual Solicitation
It usually refers to the following:
Being asked to do something sexual when you don‟t
want to
Being asked to share personal sexual information when
you don‟t want to
Being asked to talk about sex when you don‟t want to
NOTE: It does not mean that you are being solicited
for sex.
For this discussion, we call it „unwanted sexual
encounters‟
40. Involvement in unwanted sexual
encounters
Not involved
84%
Victim-only
13%
Perpetrator-only
1%
Perpetrator-
victim
2%
Unwanted sexual encounters
41. Annual prevalence rates of youth victims
of unwanted sexual encounters
Type 2006 2007 2008
ANY 15% 3% 15% 3% 18% 5%
Someone asked me to talk about sex
when I did not want to
11% 2% 13% 3% 14% 3%
Someone asked me to provide really
personal sexual questions about myself
when I did not want to tell them
11% 2% 12% 3% 13% 3%
Someone asked me to do something
sexual when I did not want to
7% 2% 8% 2% 9% 3%
43. Very / extremely upset by the
encounter – age constant (12-15 y.o.)
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Talk about sex Sexual information Do something sexual
2006
2007
2008
44. Characteristics of youth more likely to be
victims of unwanted sexual encounters
Youth Characteristics OR (95% CI)
Demographic characteristics: Age 1.3 (1.2, 1.5)
White (vs. non-White) 2.3 (1.5, 3.5)
Female 2.0 (1.5, 2.8)
Psychosocial characteristics: Perpetrator: unwanted sexual encounter 6.5 (3.9, 10.9)
Perpetrator: Relational bullying 1.8 (1.4, 2.4)
Victim: Physical harassment 2.6 (1.6, 4.0)
Victim: Relational bullying 1.7 (1.2, 2.5)
School: Poor grades (Avg: C‟s or lower) 1.7 (1.2, 2.6)
School: # of suspensions / detentions at school 0.96 (0.93, 0.99)
Alcohol use 1.7 (1.2, 2.4)
Caregiver: Poor monitoring 1.1 (1.0, 1.2)
Caregiver: Coercive discipline 1.1 (1.0, 1.2)
Above characteristics are in one multi-variate model. Odds ratios are therefore adjusted for all other characteristics shown, as well as
the following characteristics, which were not statistically significant: Hispanic ethnicity, household income, internet use, marijuana
use, carrying a weapon to school, physical harassment perpetration (offline), propensity to respond to stimuli with anger, emotional
bond with caregiver, and time
45. Annual prevalence rates of youth perpetrators
of unwanted sexual encounters
Type 2006 2007 2008
ANY 3% 1% 3% 0.7% 3% 0.4%
Asked someone to talk about sex when
they did not want to
2% 1% 2% 0.6% 2% 0.3%
Asked someone to provide really
personal sexual questions about
themselves when they did not want to
tell them
3% 1% 2% 0.5% 2% 0.4%
Asked someone to do something sexual
when they did not want to
1% 0.5% 2% 0.4% 2% 0.3%
47. Characteristics of youth more likely to be
perpetrators of unwanted sexual encounter
Youth Characteristics OR (95% CI)
Demographic characteristics: Internet use 1 hour+ per day 2.2 (1.1, 4.4)
Psychosocial characteristics: Victim: unwanted sexual
encounter
8.6 (4.8, 15.2)
Perpetrator: Physical harassment 2.1 (1.1, 4.0)
School: Weapon past 30 days 3.8 (1.3, 11.3)
Alcohol use 4.1 (2.0, 8.4)
Caregiver: Coercive discipline 1.2 (1.0, 1.5)
Above characteristics are in one multi-variate , population-average (GEE) model. Odds ratios are therefore adjusted for all other
characteristics shown, as well as the following characteristics, which were not statistically significant: youth race, age, and sex;
Hispanic ethnicity, household income, marijuana use, poor grades in school, the number of suspensions or detentions in school,
physical and relational bully victimization (offline), relational bully perpetration (offline), caregiver monitoring, emotional closeness
with caregiver, propensity to respond to stimuli with anger, and time
48. Assumptions about unwanted sexual
encounters
It means being solicited for sex
It‟s increasing over time
It‟s getting scarier / kids are more affected
Everyone‟s a hapless victim
49. None of these assumptions are supported
by the data
It means being solicited for sex
The definition is very broad; while it includes solicitations for sex, it also
includes solicitations for other things
It‟s increasing over time
Neither perpetration nor victimization rates appear to be increasing from
2006-2008
It‟s getting nastier / kids are more affected
There is no indication that young people are more likely to be upset by the
encounter now (in 2008) then they were 2 years ago (2006). If anything,
there‟s some indication that youth are *less* likely to be upset now.
Everyone‟s a hapless victim
Definitely, there are more victims (16%) than perpetrators (3%)
BUT the odds of victimization increase about 6.5 fold if you are a perpetrator
and vice versa
50. Other things to note across harassment and
unwanted sexual encounters:
Girls are more likely to be victims BUT
there is no difference by sex among
perpetrators
Girls are statistically equally as likely as
boys to sexually aggress upon others online
These kids are experiencing a multitude of
problems
(Again) Kids don‟t operate in a vacuum
51. Limitations
Findings need to be replicated – preferably in
other national data sets
Data are based upon the US. It‟s possible that
different countries would yield different rates
Non-observed data collection
Although our response rates are strong (above
70% at each wave), this still means that we‟re
missing data from 30% of participants…but
we are statistically adjusting for non-response
52. Recap: Working definition
There is no accepted definition of Internet harassment
In general: it is obnoxious behavior directed at someone
with the intent to harass or bother them.
Cyberbullying occurs over time, is repetitive, and between
people of differential strength
Unwanted sexual solicitation / encounter
Being asked to do something sexual, talk about sexual
things, or provide personal sexual information when
you don‟t want to.
It does not necessarily mean being solicited for sex
53. Recap: Prevalence rates (average across 2006-2008)
Internet harassment (ever in the past year 06-08):
Uninvolved: 62%
Victim-only: 18%
Perpetrator-only: 3%
Perpetrator-victim: 17%
Cyberbullying (ever in the past year 07-08):
Non-victim: 86%
Victim: 14%
Unwanted sexual encounter (ever in the past year 06-08):
Uninvolved: 84%
Victim-only: 13%
Perpetrator-only: 1%
Perpetrator-victim: 2%
54. Recap: Characteristics of involved youth
Characteristics Harassment
victim
Harassment
perpetrator
Cyber-
bullying
victim
USE
victim
USE
perpetrator
Internet use ↑ ↑ ↑ ↑
Age ↑ ↑ ↑
White race ↑ ↑ ↑
Income: >$75,000 (vs. <$35,000) ↑
Female ↑
Victim/perpetrator: Internet
harassment (respectively)
↑ ↑ ↑ ↑ ↑ ↑
Perpetrator: Relational bullying ↑ ↑ ↑
Perpetrator: Physical harassment ↑ ↑
Victim: Relational bullying ↑ ↑
Victim: Physical harassment ↑ ↑ ↑
55. Recap: Characteristics of involved youth
Characteristics Harassment
victim
Harassment
perpetrator
Cyber-
bullying
victim
USE
victim
USE
perpetrator
School: Weapon past 30 days ↑ ↑
School: Poor grades (<=C) ↑
School: # detentions / suspensions ↓
Propensity to respond to stimuli
with anger
↑
Alcohol use ↑ ↑ ↑
Caregiver: emotional closeness ↑
Caregiver: coercive discipline ↑ ↑
Caregiver: poor monitoring ↑
Time ↓
56. Recap: Research supporting and refuting
assumptions about Internet victimization
Assumption: Victimization is increasing
Rates of victimization appear to be holding
steady (and maybe in some cases decreasing)
from 2006-2008
Assumption: Victimization is getting nastier
At least as measured by rates of distress –
victimization distress rates appear to be holding
steady (and maybe in some cases decreasing)
from 2006-2008
57. Takeaways
As professionals we need to be able to sit with these two
“competing” realities:
Like other forms of victimization, bullying and
unwanted sexual encounters online can be distressing
for youth who experience them.
We need to do a better job of identifying these
youth and getting them into services (e.g., therapy).
We need to recognize also that:
The majority of youth are not being victimized online,
The majority who are, are not seriously upset by it..