WWWDOT Worksheet: Reading report 2
Name: _______Student Name_________ Group: ___ 2____
Date: ____16th July 2020___
Who is the author of the website (and what credentials do they have? Do credentials match
up with the content?)
The authors’ names are Eghtesadi, Marzieh and Florea, Adrian. Eghtesadi, M. is a doctor from
Department of Clinical Neurosciences, Chronic Pain Centre of CHUM, Montréal, Canada and
Florea, A. is working for Department of Emergency Medicine, McGill University and Health
Informatics at Johns Hopkins University. Besides, from the PDF of this article has the credential
from The Canadian Public Health Association 2020 which is tightly connected with the content
of the article about how to disseminate professional medical information through social media.
Plus, the website has the credential by 2020 Springer Nature Switzerland AG. All of the above
let me believe this is an authoritative and trustworthy article and website.
Why did the author write this material?
The authors want to point out that those professional medical institutions or doctors can use
online social media to communicate or share the right information although there are some
problems with social media. Especially, during the pandemic, this approach is more efficient
than traditional media because there are large amount users who follow these social media like
Facebook, Reddit and TikTok.
When was it written and/or updated?
This article was published on June 09, 2020 that means it advance up with the times and
integrated with this year’s global epidemic.
Does it help to meet my needs?
Yes, it does help my needs. When I started doing a search through the online library, my purpose
was to find out what positive influence can TikTok bring to society besides entertainment.
Fortunately, this article is exactly what I need; furthermore, it has more details and information
Organization of the website. (Describe in detail)
The organization of this article’s website is pretty good and I will describe the details as much as
I can. First of all, on the top of the site where it shows the citation of the article and it offers links
of two authors, Canadian Journal of Public Health, published date, and cite this article that it is
convenient for readers to check more information. Also it lists how many accesses and altmetric,
beside altmetric we can click Metrics that have the situation of this article through social media
like sharing by 3 tweeters. Second, there is an abstract that tells us the authors’ purpose and main
idea. Then, it is the content of the article, reference, acknowledgements, author information, and
little more additional information. Third, there is the section about this article includes citations
and keywords. At the end of this website, there are more links about this site itself and its
credential. At last, on the right of the page is another section that supports us to check all the
details of this article, author, reference, and advertisement. But when I download the PDF of this
article, the format is different. For example, it has the journal name, published year, page
number, and DOI link on the page header. Different from the website, on PDF there are some
little marks after the authors’ names; then, we can find these marks and their information on the
To-do list for the future
Definitely, this article and website is trustworthy because it is from university library and this
journal is peer reviewed by other experts. After I browsed the home page of the Springer Natural,
I believe it will bring and disseminate more essential information because it keeps updating the
journals and it connects with social media like Tweeter, Facebook and LinkedIn. It could be a
professional place for experts and authors to discuss and sharing their theories as well.
Source: Provide a citation in APA format for the text you have chosen.
Eghtesadi, M., & Florea, A. (2020). Facebook, Instagram, Reddit and TikTok: a proposal for
health authorities to integrate popular social media platforms in contingency planning amid
a global pandemic outbreak. Canadian Journal of Public Health, 111(3), 389–391.
Outline: Make an outline of the main ideas and supporting points in the reading.
This paragraph is a summary of the whole article that authors want to talk about communicating
and sharing critical information through online social media and it is an efficient method during
Main idea: It is difficult to analyze too many mixed messages during the pandemic.
Major Supporting Detail: wave of information that has been overwhelming within the
Minor Supporting Detail – Our email inboxes have been flooded
– Email from IT are not spam or from hacked accounts
Main idea: More health workers believe social medial is an important role
Major Supporting Detail: healthcare communities become more popular on Facebook
Minor Supporting – In the Canadian province of Québec, over 5000 physicians
– Members are more active in online virtual communities
Main idea: Facebook has more functions than normal email
Major Supporting Detail: physicians could subscribe to medical forums
Minor Supporting – “like, join, and pages”
– The pages of the World Health Organization and the Centers for
Disease Prevention and Control
Major Supporting Detail: integrated private chat function
Minor Supporting – Facebook Messenger
– Avoid the frustrations related to the “reply to all” function in
Main idea: social media platforms have more functions when disseminating information
Major Supporting Detail: social media platforms can let physicians act as “influencers”
Minor Supporting – “someone (or something) with the power to affect the buying
habits or quantifiable actions of others by uploading some
form of original content to social media platforms”
– Physician-managed “pages” are trustworthy sources
– Possibility for administrators to pay in order to increase the
visibility of their content
Main idea: Make sure social media does work
Major Supporting Detail: translates into actual changes in population behaviours and
disease outcome from social media
Minor Supporting –Target age group and geographical location, to ArcGIS, the
world’s leading mapping
– important of social distance posted on Facebook
Main idea: introduce the Reddit and its effect
Major Supporting Detail: Scale and function of Reddit
Minor Supporting – 6th most frequently visited website in the United States
– subreddit community with the word “coronavirus” or “covid”
was over 1.6 million
– Users browse topics according to what is hot, new, top,
controversial or rising
Major Supporting Detail: there is useful and scientifically sound content on Reddit
Main idea: introduce TikTok and its influence during pandemic
Major Supporting Detail: short-form mobile videos and has over 1 billion users and is
available in 150 different countries since 2017.
Minor Supporting – a song from Vietnam promoting preventive measures
Major Supporting Detail: it is a powerful tool for physicians working in community youth
Main idea: Although there are some issues exist in social media, authors still encourage medical
workers or institutions to use these platforms.
Major Supporting Detail: One of the problems with popular social media is lack scientific
oversight, generating noise and false information.
Minor Supporting – healthcare workforce cannot filter the spread of
Major Supporting Detail: social media is not generalizable to the whole population
Minor Supporting – older adults don’t use social media to communicate
Major Supporting Detail: authors encourage our regulatory associations to guide our
impact within these online social media
It took me about half hour to go through the whole article because I tried to understand it
completely clear. For the first time, I did a quick reading and got 90% information of it, and then
I wrote down all vocabulary that I didn’t know. I read the article again and confirmed I
understand everything after using the dictionary to translate them into Chinese. I think this article
is not too difficult for me to read because I am using these social media in my life and already
receive lots of news about coronavirus; therefore, it is convenient for me to get the purpose of the
RE S EAR CH | R E P O R T S
Accelerating extinction risk from
Mark C. Urban*
Current predictions of extinction risks from climate change vary widely depending on the
specific assumptions and geographic and taxonomic focus of each study. I synthesized
published studies in order to estimate a global mean extinction rate and determine which
factors contribute the greatest uncertainty to climate change–induced extinction risks.
Results suggest that extinction risks will accelerate with future global temperatures,
threatening up to one in six species under current policies. Extinction risks were highest in
South America, Australia, and New Zealand, and risks did not vary by taxonomic group.
Realistic assumptions about extinction debt and dispersal capacity substantially increased
extinction risks. We urgently need to adopt strategies that limit further climate change
if we are to avoid an acceleration of global extinctions.
area relationships (5%), or expert opinion (4%).
Species were predicted to become extinct if
their range fell below a minimum threshold.
An important caveat is that most of these models ignore many factors thought to be important
in determining future extinction risks such as
species interactions, dispersal differences, and
Overall, 7.9% of species are predicted to become extinct from climate change; (95% CIs, 6.2
and 9.8) (Fig. 1). Results were robust to model
type, weighting scheme, statistical method, potential publication bias, and missing studies (fig. S1
and table S2) (6). This proportion supports an
estimate from a 5-year synthesis of studies (7). Its
divergence from individual studies (1–4) can be
explained by their specific assumptions and taxonomic and geographic foci. These differences
provide the opportunity to understand how divergent factors and assumptions influence extinction
risk from climate change.
The factor that best explained variation in
extinction risk was the level of future climate
change. The future global extinction risk from
climate change is predicted not only to increase
but to accelerate as global temperatures rise (regression coefficient = 0.53; CIs, 0.46 and 0.61)
(Fig. 2). Global extinction risks increase from
Overall extinction risk = 7.9% (95% CI: 6.2, 9.8)
# of studies
Fig. 1. Histogram of percent extinction risks from
climate change for 131 studies. Percent extinction
risk refers to the predicted percent of species extinctions in each study, averaged across all model
assumptions. The meta-analysis estimated mean
with 95% CIs is also shown.
*Corresponding author. E-mail: firstname.lastname@example.org
Department of Ecology and Evolutionary Biology, University
of Connecticut, 75 North Eagleville Road, Unit 3043, Storrs,
CT 06269, USA.
Percent extinction risk
1 MAY 2015 • VOL 348 ISSUE 6234
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e critically need to know how climate
change will influence species extinction
rates in order to inform international
policy decisions about the biological
costs of failing to curb climate change
and to implement specific conservation strategies
to protect the most threatened species. Current
predictions about extinction risks vary widely,
suggesting that anywhere from 0 to 54% of species could become extinct from climate change
(1–4). Studies differ in particular assumptions,
methods, species, and regions and thus do not
encompass the full range of our current understanding. As a result, we currently lack consistent,
global estimates of species extinctions attributable to future climate change.
To provide a more comprehensive and consistent analysis of predicted extinction risks
from climate change, I performed a meta-analysis
of 131 published predictions (table S1). I focused
on multispecies studies so as to exclude potential biases in single-species studies. I estimated
the global proportion of species threatened in
a Bayesian Markov chain Monte Carlo (MCMC)
random-effects meta-analysis that incorporated variation among and within studies (5)
and with each study weighted by sample size
(6). I evaluated how extinction risk varied depending on future global temperature increases,
taxonomic groups, geographic regions, endemism, modeling techniques, dispersal assumptions, and extinction thresholds. I used credible
intervals (CIs) that do not overlap with zero and
a deviance information criterion (DIC) greater
than four to assess statistical support for factors.
The majority of studies estimated correlations
between current distributions and climate so
as to predict suitable habitat under future climates. A smaller number of studies determined
extinction risks by using process-based models of physiology or demography (15%), species-
2.8% at present to 5.2% at the international policy target of a 2°C post-industrial rise, which
most experts believe is no longer achievable (8).
If the Earth warms to 3°C, the extinction risk
rises to 8.5%. If we follow our current, businessas-usual trajectory [representative concentration
pathway (RCP) 8.5; 4.3°C rise], climate change
threatens one in six species (16%). Results were
robust to alternative data transformations and
were bracketed by models with liberal and conservative extinction thresholds (figs. S2 and S3
and table S3).
Regions also differed significantly in extinction risk (DDIC = 12.6) (Fig. 3 and table S4).
North America and Europe were characterized by the lowest risks (5 and 6%, respectively), and South America (23%) and Australia
and New Zealand (14%) were characterized by
the highest risks. These latter regions face noanalog climates (9) and harbor diverse assemblages of endemic species with small ranges.
Extinction risks in Australia and New Zealand
are further exacerbated by small land masses
that limit shifts to new habitat (10). Poorly studied
regions might face higher risks, but insights
are limited without more research (for example, only four studies in Asia). Currently, most
predictions (60%) center on North America
and Europe, suggesting a need to refocus efforts toward less studied and more threatened
Endemic species with smaller ranges and certain taxonomic groups such as amphibians and
reptiles are predicted to face greater extinction
risks (11, 12). I estimated that endemic species
face a 6% greater extinction risk relative to models
that include both species endemic and nonendemic to the study region (DDIC = 8.3). Extinction risks also rose faster with preindustrial
temperature rise for models with endemic species (DDIC = 8.2) (fig. S4). In contrast to predictions, extinction risks did not vary significantly
by taxonomic group (DDIC = 0.7) (Fig. 4). One
explanation is that trait variation at finer taxonomic scales might play a more important role in
modulating extinction risks (13). Also, typical approaches for quantifying extinction risks likely
do not capture the full range of differences among
R ES E A RC H | R E PO R TS
Key model assumptions altered predictions
of future extinction risk. For instance, extinction debts occur when species decline to the
point that they are committed to extinction, but
not yet extinct (14). Studies differed in how much
Target RCP 6.0
Pre-industrial Temperature Rise (C)
ing the extinction threshold from 100% (no extinction debt) to 80% increased risk from 5 to
15% (DDIC = 144.1) (Fig. 4), and lower thresholds increased the rise in extinction risk with
future temperatures (interaction DDIC = 5.9)
(fig. S2). The applicability of these thresholds
will depend on species-specific characteristics
such as generation time and initial population
size. We urgently need to understand how range
reductions determine future extinction risk better in order to predict accurately both the number and timing of future extinctions (15).
Species must disperse into newly suitable habitats as fast as climates shift across landscapes
(16, 17). Modelers variously assume no dispersal,
dispersal only into contiguous habitats, dispersal
based on each species’ ability, or universal dispersal regardless of distance or ability. Modelers
usually assume no dispersal and universal dispersal and presume that the true value lies between these extremes. I found that assumptions
about dispersal significantly affected extinction
risks (DDIC = 68.5) (Fig. 4). Species-specific
dispersal increased extinction risk from 6%, assuming universal dispersal to 10%. Assuming
no dispersal increased risk further to 12%. Extinction risks increase more rapidly with temperature rise assuming no- and species-specific
dispersal (interaction DDIC = 6.1) (fig. S5). Incorporating more realistic species-specific dispersal
Fig. 3. Predicted extinction risks from climate change differ by region. The highest risks characterized South America, Australia, and New Zealand
(14 to 23%), and the lowest risks characterized North America and Europe (5 to 6%). Colors indicate relative risk. Bar graphs with 95% CIs and number
of studies (n) are displayed.
1 MAY 2015 • VOL 348 ISSUE 6234
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Fig. 2. Predicted extinction
risks from climate change
accelerate with global
temperature rise. The gray
band indicates 95% CIs.
Preindustrial rise was
calculated by using standard
methods (27). Circles indicate posterior means with
area proportional to log10
sample size (bottom left,
key). Extinction risks for four
scenarios are provided: the
current postindustrial temperature rise of 0.8°C (5),
the policy target of 2°C, and
RCPs 6.0 and 8.5.
habitat loss was assumed to commit a species
to extinction, commonly applying habitat loss
thresholds of 100, 95, and 80%. Extinction thresholds were second only to expected climate change
in explaining variable extinction risks. Decreas-
RE S EAR CH | R E P O R T S
Fig. 4. Predicted
extinction risks from
depend on model
model support (DDIC >
4) for each factor separately, and number of
studies is included in
parentheses. Categories within each factor
are listed in order of
risk. The gray vertical
reference line indicates
mean overall extinction
risk. Bars represent
that influence them. However, I emphasize that
extinction risks are likely much smaller than the
total number of species influenced by climate
change. Even species not threatened directly by
extinction could experience substantial changes
in abundances, distributions, and species interactions, which in turn could affect ecosystems and
their services to humans (19). Already, changes in
species’ phenologies, range margins, and abundances are evident (20, 21). Extinctions, although
still uncommon, are increasingly attributed to climate change (22).
At the same time, we must cautiously interpret
the predictions underlying this meta-analysis.
The majority of studies extrapolate correlations
between current climate and species distributions to novel co …
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