Showing posts with label predicts. Show all posts
Showing posts with label predicts. Show all posts
European Union predicts shortfall of one million doctors and nurses by
2020

European Union predicts shortfall of one million doctors and nurses by 2020

Overseas-trained doctors accounted for 37% of UK-registered doctors in 2008.

25% of practicing physicians in the United States and 28% of U.S. medical residents come from abroad. Of these, 25% were trained in India and Pakistan.

At the same time, U.S. lawmakers suggest we save money by training fewer doctors, according to the UCMC Dean editorial.

The mounting shortage of physicians nationwide is expected to grow to 90,000 by 2020.

Compare this to China where typically 3-4 newly qualified doctors will rent a flat together to defray their costs (BusinessWeek). The U.S. has one public health professional for every 635 people. The rate in China is one per 7,000.

Comments from Google Plus:

Tim Sturgill - Makes you really wonder about the EU and US physician shortcomings—what are we going to do? Medical Home? I don't see this ideal broaching physicianopenia. I'm beginning to see ED impressions that list "failure or failed primary care." Is there a trifecta coming: Physicianopenia, Medical Home, and Readmissions?

Colin Son - Wow even more than the percentage of foreign grads in the United States http://www.foreignpolicy.com/articles/2010/06/11/countries_without_doctors

Ves Dimov - The mounting shortage of U.S. physicians nationwide is expected to grow to 90,000 by 2020.

References:

Off the record Europe: Workforce planning. British Medical Association, 2011.
Countries Without Doctors?
Image source: Openclipart.org

The Number of Tweets Predicts Future Citations of a Specific Journal Article

Citations of journal articles and the impact factor are widely used measures of scientific impact. Web 2.0 tools such as Twitter, Facebook, blogs and social bookmarking tools provide the possibility to construct article-level or journal-level metrics to gauge impact and influence.

Between 2008 and 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were data mined.The tweets were compared to subsequent citation data 17-29 months later.

4,000 tweets cited 280 JMIR articles. The distribution of tweets followed a power law, with most tweets sent on the day when an article was published (44% of all tweets in a 60-day period) or on the following day (16%), followed by a rapid decay.

The Pearson correlations between "tweetations" and regular citations were moderate and statistically significant (0.42 to 0.72).

Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles.

Top-cited articles could be predicted from top-tweeted articles with 93% specificity and 75% sensitivity.

Tweets can predict highly cited articles within the first 3 days of article publication.

Social media activity may:

- increase citations
- reflect the underlying qualities of the article

Social impact measures based on tweets are proposed to complement traditional citation metrics. The study author proposed a "twimpact" factor that measures uptake and filters research resonating with the public in real time.

After the initial publication, some science blogs have pointed out potential issues and conflicts of interests in relation to the topic and the single author who is also the founder, owner, and Editor-in-Chief of the journal. You can find more by performing a Google search for "twimpact" factor or checking the references section at the end of this post. Overall, I think this is an interesting concept and Gunther Eysenbach did a great job focusing the attention of the journal publishers on Twitter and Facebook as distribution channels that can also guide in measuring the impact of their articles.

References:

Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact. Gunther Eysenbach. J Med Internet Res 2011;13(4):e123.

New research plus twitter. Does it make a difference in the clinic. Heidi Allen Digital Strategy in Health.

'Highly Tweeted Articles Were 11 Times More Likely to Be Highly Cited'. The Atlantic.

Twimpact factors: can tweets really predict citations? BMJ.

Tweets, and Our Obsession with Alt Metrics

Image source: Twitter.com.

Comments from Twitter:

@paediatrix:  Interesting. Makes sense

Harris Lygidakis @lygidakis: And Twimpact Factor is a good sign of what's ahead!

Glycated hemoglobin as a diagnostic test for diabetes predicts mortality more accurately than fasting glucose

Fasting glucose is the standard measure used to diagnose diabetes in the United States. Recently, glycated hemoglobin was also recommended for this purpose.

The glycated hemoglobin value at baseline was associated with newly diagnosed diabetes and cardiovascular outcomes.

For glycated hemoglobin, values of less than 5.0%, 5.0-5.5%, 5.5-6.0%, 6.0-6.5%, and 6.5% or greater, the hazard ratios for diagnosed diabetes were 0.52, 1.00, 1.86, 4.48, and 16.47, respectively.

For coronary heart disease, the hazard ratios were 0.96, 1.00, 1.23, 1.78, and 1.95, respectively. The hazard ratios for stroke were similar.

In contrast, glycated hemoglobin and death from any cause were found to have a J-shaped association curve.

The association between the fasting glucose levels and the risk of cardiovascular disease or death from any cause was not significant.

In this community-based population of nondiabetic adults, glycated hemoglobin was associated with a risk of diabetes and more strongly associated with risks of cardiovascular disease and death from any cause as compared with fasting glucose. These data add to the evidence supporting the use of glycated hemoglobin as a diagnostic test for diabetes.

References:
Image source: OpenClipArt.org, public domain.
Your smartphone use predicts your social life, travel, risk of disease
- even political views

Your smartphone use predicts your social life, travel, risk of disease - even political views



The Really Smart Phone: Researchers are harvesting a wealth of intimate detail from cellphone data, uncovering the hidden patterns of social lives, travels, risk of disease - even political views.