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Introduction: The CJEM Social Media Team was created in 2014 to assist the journal with the dissemination of its research online. It consists of two Social Media Editors (Junior and Senior) and a team of volunteer medical students and residents to assist their work. Collaborative promotional agreements were developed to promote CJEM articles on the Skeptics’ Guide to Emergency Medicine (SGEM) podcast through the ‘Hot off the Press’ (HOP) series and the CanadiEM blog through an infographic series. Methods:CJEM papers were selected for promotion by the Team based on their perceived interest to the online community of emergency physicians. Altmetric scores, which are a measure of online dissemination derived from a weighted algorithm of social media metrics, were collated for articles promoted using the SGEM HOP or CanadiEM blogs. A control group was created using the articles with the top two Altmetric scores in each CJEM issue in 2015 and 2016. Erratum, Letters, and articles written by the social media editors were excluded from the control groups. The success of the social media promotion was quantified through the measurement of Altmetric scores as of January 1, 2017. Unpaired two-tailed t-tests with unequal variance were used to test for significant differences. Results: 106 and 82 eligible articles were published in 2015 and 2016, respectively. Four articles in 2015 and two articles in 2016 were excluded from the control groups because they were written by the social media editors. SGEM HOP podcasts promoted one article in 2015 and five articles in 2016. CanadiEM infographics promoted three articles in 2015 and eight articles in 2016. No articles were promoted in both series. The average Altmetric score was higher for SGEM HOP (61.0) than CanadiEM Infographics (31.5, p<0.04), 2015 controls (15.8, p<0.01), and 2016 controls (13.6, p<0.01). The average Altmetric score for CanadiEM Infographics was higher than 2015 controls (p<0.04) and 2016 controls (p<0.02). There was no significant difference between the control groups. Conclusion: The results suggest that collaborating with established social media websites to promote CJEM articles using podcasts and infographics increases their social media dissemination. Given the nonrandomized design of these results, causative conclusions cannot be drawn. A randomized study of the impact of social media promotion on readership is underway.
In fall 2011, cotton and soybean consultants from Arkansas, Louisiana, Mississippi, and Tennessee were surveyed through direct mail and on-farm visits, and rice consultants from Arkansas and Mississippi were surveyed through direct mail to assess the importance and level of implementation of herbicide resistance best management practices (HR-BMPs) for herbicide-resistant weeds. Proper herbicide timing, clean start with no weeds at planting, application of multiple effective herbicide modes of action, use of full labeled herbicide rates, and prevention of crop weed seed production with importance rating of ≥ 4.6 out of 5.0 were perceived as the most important HR-BMPs in all crops. Purchase of certified rice seed was on 90% of scouted hectares. In contrast, least important HR-BMPs as perceived by consultants with importance ratings of ≤ 4.0 in cotton, ≤ 3.7 in rice, and ≤ 3.8 in soybean were cultural practices such as manual removal of weeds; tillage including disking, cultivation, or deep tillage; narrow (≤ 50 cm)-row crops, cover crops, and altered planting dates. Narrow crop rows and cover crops in cotton; altered planting dates in cotton and soybean; and cleaning of farm equipment and manual weeding in rice and soybean is currently employed on ≤ 20% of scouted hectares. Extra costs, time constraints, adverse weather conditions, lack of labor and equipment, profitability, herbicide-related concerns, and complacency were perceived as key obstacles for adoption of most HR-BMPs. With limited adoption of most cultural practices that reduce risks of herbicide-resistant weeds, there are opportunities to educate growers concerning the proactive need and long-term benefits of adopting HR-BMPs to ensure sustainable weed management and profitable crop production.
The relationship between partner alcohol use and violence as risk factors
for poor mental health in women is unclear.
To describe partner-related and other psychosocial risk factors for
common mental disorders in women and examine interrelationships between
Data are reported on 821 women aged 18–49 years from a larger population
study in north Goa, India. Logistic regression models evaluated the risks
for women's common mental disorders and tested for mediation effects in
the relationship between partner alcohol use and these disorders.
Excessive partner alcohol use increased the risk for common mental
disorders two- to threefold. Partner violence and alcohol-related
problems each partially mediated the association between partner
excessive alcohol use and these mental disorders. Women's own
violence-related attitudes were also independently associated with
Partner alcohol use, partner violence and women's violence-related
attitudes must be addressed to prevent and treat common mental disorders
This study intended to create symptom-based triage algorithms for the initial encounter with terror-attack victims. The goals of the triage algorithms include: (1) early recognition; (2) avoiding contamination; (3) early use of antidotes; (4) appropriate handling of unstable, contaminated victims; and (5) provisions of force protection. The algorithms also address industrial accidents and emerging infections, which have similar clinical presentations and risks for contamination as weapons of mass destruction (WMD).
The algorithms were developed using references from military and civilian sources. They were tested and adjusted using a series of theoretical patients from a CD-ROM chemical, biological, radiological/nuclear, and explosive victim simulator. Then, the algorithms were placed into a card format and sent to experts in relevant fields for academic review.
Six inter-connected algorithms were created, described, and presented in figure form. The “attack” algorithm, for example, begins by differentiating between overt and covert attack victims (A covert attack is defined by epidemiological criteria adapted from the Centers for Disease Control and Prevention (CDC) recommendations). The attack algorithm then categorizes patients either as stable or unstable. Unstable patients flow to the “Dirty Resuscitation” algorithm, whereas, stable patients flow to the “Chemical Agent” and “Biological Agent” algorithms. The two remaining algorithms include the “Suicide Bomb/Blast/Explosion” and the “Radiation Dispersal Device” algorithms, which are inter-connected through the overt pathway in the “Attack” algorithm.
A civilian, symptom-based, algorithmic approach to the initial encounter with victims of terrorist attacks, industrial accidents, or emerging infections was created. Future studies will address the usability of the algorithms with theoretical cases and utility in prospective, announced and unannounced, field drills. Additionally, future studies will assess the effectiveness of teaching modalities used to reinforce the algorithmic approach.
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