Category: Resources

Disseminating my research

Publication in a peer-reviewed journal is no longer sufficient – research findings need to be disseminated more broadly to ensure (and demonstrate) that they have impact. This means that once I’ve submitted an article for publication I immediately start working on the dissemination plan (if I haven’t already done it as a form of ‘productive procrastination‘!)

There is no one-size-fits-all approach. However, I do have a standard list of dissemination options and a general process that I use. Here it is, in case it is useful for you:

Step 1: Write different versions of your article (during article writing/immediately after submission)

  • Blog post – I usually start by writing a blog post, and this is an excellent article about how to turn your journal article into a blog post, but I’ve also found this one useful.
  • Press release – The University press office has been really helpful in structuring the story and using appropriate language for my press releases in the past (although they sometimes need help making sure the essential message isn’t lost).
  • Talking points – talking points are a great way to prepare for a media interview. In addition, the process of identifying and refining my talking points helps to identify and refine the message, audience and purpose for my dissemination strategy. I usually come up with about 5 talking points, for example: a short sentence and a short paragraph about the main result(s), a short sentence and a short paragraph about the implications, and a short sentence about what might come next.

Step 2: Circulate your pitch (before acceptance)

You may need to modify your pitch for each of the sources below, but you can base all of them on your press release. You need to circulate your pitch to these sources before your article is accepted, because often things move quite quickly after acceptance and you want to have time to work with these people to craft the best piece, and to coordinate the release dates with them.

  • Send a pitch to The Conversation (to do this you need to log in, and use the link on the left hand side of the dashboard)
  • Send a pitch to podcasts that might be interested. Podcasts usually have a longer lead time than the general media, so better to contact them early. There are some health-specific ones (e.g. 2SER Think:Health, the Research Roundup podcast by PC4) or more general ones, such as the University of Sydney podcast ‘Open for Discussion‘.
  • Send a pitch to any other magazine, website, etc that might be relevant. For example, in the past I’ve published summaries in Cancer Professional and have flagged oncologynews.com.au and Croakey as a possible media to approach in the future.

Step 3: Prepare for release (once accepted)

Once you know your article is accepted you should get a timeline for when it will be released. At this point you should let anyone who you’ve worked with on an article (e.g. the Conversation, etc) know the date and coordinate the release. You can also:

  • Contact relevant journalists with your press release. The press office can do this for you, and/or you can use informal approaches such as twitter (list of tweeting journalists below)
  • Contact relevant professional associations about circulating a short article about your research in their newsletter etc. I usually approach groups like the HSRAANZ, AHES, ESA.
  • Finalise your talking points for any media interviews. This includes the talking points drafted earlier, as well as notes on the different ways journalists or readers could misunderstand my research, and any sticky questions I’m nervous about. Then I draft responses to these (which I usually never need, but it makes me feel less nervous knowing I’m prepared).

Step 4: Disseminate (once published)

At last! Today is the day to…

  • Publish your blogpost on your blog
  • Publish your blogpost on LinkedIn
  • Write a post with a link to your blogpost (on your blog or LinkedIn) to Facebook
  • Tweet about your research – over the day or two after publication I usually tweet a link to the original article (with a sentence summarising the main finding), tweet a link to my blog post, tweet a link to any companion pieces (e.g. an article in The Conversation), and retweet any press coverage I get. I haven’t tried this yet, but I was recently told to tag relevant journalists in some of these tweets, and so I’ve compiled the following list of potential options:

Step 5: Tracking your dissemination

As we increasingly need to report our impact, it will become more important to be able to track how and to whom our research was disseminated. Tools like Google Alerts and Altmetrics can be very useful, but I’m also going to try and take screenshots/links/copies of any press coverage etc that I get and save them in the project folder, so that I can easily find them later.

Practical resources for analysing your first DCE

 

I’m relatively new to discrete choice experiments and have really enjoyed learning about the different analysis approaches and techniques used. It is such a rapidly evolving field and there is always something new to learn. While there is a lot happening to push the boundaries, I’ve recently been helping a couple of people with the analysis of their first DCE. While a lot of your analysis approach should be worked out before you begin the DCE,  when you get to the point of actually doing the analysis for the first time there is a whole lot of stuff around which commands to use that you might still need help with. I realised there are some references I just keep recommending and coming back to, so I’ve shared them here maybe you’ll find them helpful too. [Note: this post is updatted as I come across new resources].

General guidance

It often helps to know at the start what you are aiming to achieve at the end. I think this is a nice example of describing the methods and assumptions of a DCE around parental preferences for vaccination programs really clearly and succinctly. The other general information I refer people to is the ISPOR Analysis of DCE guidelines, which include the ESTIMATE checklist of things to consider when justifying your choice of approach.

Analysis approach

When I did the DCE course run through HERU in Aberdeen it was suggested that the typical approach to considering analysis of DCEs was to be to start with a simple model and then use more complex models to address specific issues that arise with your data or relate to your research question. This commonly means starting with a conditional logit model, and then considering options such as mixed logit and latent class analysis. The ISPOR Analysis of DCE guidelines have clear descriptions of the theory and assumptions of these approaches, and I found this paper interesting in comparing mixed logit and latent class approaches.

Analysis code

I am originally a SAS user, and so when I first started analysing DCE data I assumed I would do so in SAS. However, after much investigation I’ve realised this is easier said that done and have now moved to using STATA for the DCE analysis, although I’m still much more comfortable doing the data management and preparation in SAS. Using two different packages is time consuming, clunky and the opposite of “reproducible research”, so my next step is to convert managing my DCE data AND analysis in R. I haven’t got very far, so if anyone knows any good packages then please pass them on! I promise to update this page if I find something useful.

  • SAS

It is straight forward to run a conditional logit in SAS using PROC MDC (user guide). Some resources I found helpful to implement PROC MDC is this example code for conditional logit with PROC MDC and this SAS user group paper “Discrete choice modelling with PROC MDC”. The error message I’ve had most often in doing this analysis is “CHOICE=variable contains redundant alternatives” which relates to the data looking like people have chosen more than one option in a choice set. If you get this, check the cleaning and the sorting of your data!

You can do effectively the same analysis using PROC PHREG, as described by this technote, plus there is a suite of marketing research guides that describe various ways to analysis discrete choice data.

Moving on from conditional logit to mixed logit or latent class analysis is more difficult in SAS. There is a guide in this video to running conditional logit models and mixed logit models (using PROC MDC, starts at 5:30 minutes), although I could never get their mixed logit method to work (entirely possible due to user error!). I did also contact the SAS helpdesk and they said it would be difficult, but recommended using PROC BCHOICE (Bayesian Choice) for mixed logit analysis with DCE data that has multiple choice sets per participant. There is some documentation here and a worked example here.  Again, I never really got this to work but it could be my mistake.

  • STATA

Having faffed around in SAS for long enough, I caved in and transitioned to using STATA like everyone else in my research group! I found this a really nice introductory, step by step guide to analysis in STATA, including data set up and Conditional Logit and Mixed logit options. There is also this article which is a guide to analysing DCE data and model selection, and includes STATA code (as well Nlogit and Biogene) in the supplementary material. Finally, this working paper is useful for describing the theory and code for doing more advanced models, like Mixed Logit and Latent Class analysis in STATA, although the code isn’t annotated which I found frustrating as a new STATA user. I haven’t used it yet, but there was a STATA newsletter article about using the margins option to interpret MIXL choice model results, which could be useful.

For latent class analysis is STATA I found this article in the STATA journal a useful description of the command, and this was a nice example of a paper that used mixed logit and latent class models and wrote them up clearly. Finally, these three articles (one, two, three) seemed like good examples of calculating and displaying relative importance graphs.

  • R

I’m keen to analyse my next DCE in R, so have started looking at how I might do this. I have found the following resources, but if anyone has any experience with DCEs in R then please get in touch!

  • Two papers by Aizaki and Aizaki & Nishimura on designing DCEs in R, and including analysis using conditional logit models
  • Example R code and case study of mixed logit model with multiple choices per respondent, including analysis and helpful tips, written by Kenneth Train and Yves Croissant
  • An mlogit package for analysing DCE data in R, as described in Kenneth Train (2009)
  • Thanks to Nikita Khanna for pointing me to this paper & code for doing sample size calculations for a DCE in R.
  • There is also the Apollo package in R, developed by the group at the Choice Modelling Centre at the University of Leeds, with a website & manual available.

My experience of mentoring

I have been asked a few times recently to give presentations on my experience of mentoring as an early career researcher. I have been lucky to have had a number of formal and informal mentoring experiences over the last 10 years, and some have been more successful than others.

Business Idea, Planning, Business Plan

I’ve been mentored by bosses, colleagues and friends of friends. One of the most influential arrangements has been the HSRAANZ mentoring scheme, which I’ve participated in twice: first as a PhD student who was close to finishing but didn’t know what to do next, and more recently as an early career researcher wondering how to become a mid-career researcher. In both cases I was paired with a senior health economist in a different organisation and different area of health economics to myself, but both were very experienced academics with valuable advice.

Being mentored as a PhD student. I had taken a number of sideways steps into health economics, so didn’t feel like I was on a clear career path. In particular, my main interest was oncology, but everyone around me seemed to specialise in a methodology rather than a clinical area and I wasn’t sure what I should do next. I sent my CV to my mentor and we had a long and broad discussion of my options and the various opportunities available to me. He asked about my wishlist for the next 5 years and, having heard it, suggested that to get everything on it I should probably look overseas. I’m so glad he did, because I got my dream postdoctoral fellowship in the health economics of cancer at the National Cancer Registry in Ireland. My mentor and I only had that one (long) conversation, but it changed my life!

Being mentored as a postdoc. I reapplied for a mentor through HSRAANZ half way through my second postdoc. I was wondering how to move from being an early career researcher ‘with potential’ to being a mid-career research with demonstrated value. This relationship was structured differently, with a series of wide ranging chats over monthly coffee meetings. I found it really helpful to get a fresh perspective on what being a mid-career researcher looked like, and types of roles and responsibilities I should be aiming for. It was also great to have another set of eyes looking out for opportunities that might be valuable, and to introduce me to a wider network.

As part of my postdoc I also get mentoring with two (very) senior UTS academics. Although they are from outside my field, they are excellent at explaining the politics of the university system and academia more generally. They have given me a fresh perspective on strategic career planning and how to package my research for impact and a more general audience.

Most helpful aspects of being mentored: In both the HSRAANZ mentoring scheme and my other mentoring experiences, being able to talk to someone about the big picture has been invaluable. In particular, talking to someone outside my organisation, so they weren’t constrained to what else was happening in the office (e.g. what projects are coming up, the development needs of other people, etc.). Hearing how things work in different organisations was also great, as I’ve had limited exposure to different academic environments. And finally, having another set of yes to look out for opportunities for me, but also to be able to review grant application, look for gaps in my CV and give me fresh feedback has been fabulous.

Top tips to make the most of being mentored:

  1. Push your mentor to make sure meetings happen. In almost all my mentoring experiences I’ve had to be proactive. My mentors are senior academics, which means they are busy. So be organised – set meeting times with calendar invites, organise a room/cafe/teleconference line, send an agenda prior to the meeting, etc.
  2. Use your CV as a starting point for the first meeting. Send an updated CV to your mentor at least a week before the meeting and ask them to review it. Then use the meeting time to go over it and get feedback on the strengths and weaknesses they perceive, and how they would see you as a job applicant. Then as they get to know you they can give advice on how to adjust your CV to reflect your true skills and knowledge, and also be on the look out for opportunities to fill in gaps or show off your strengths.
  3. Be honest, so that you can get the most out of them. Although it is easy to fall into the trap of trying to impress them, you actually want them to give you advice for the real you – even if that means you’re unorganised, un-confident and/or unsure what you’re doing.
  4. Have a defined question you want to work through with them. Even if it is a big one (what should I do after my PhD!) this gives structure to the relationship, and also helps you identify when you’ve achieved your goal.

Being mentored has given me a broader perspective, a wider network of contacts and access to different resources and opportunities. I will continue to seek mentoring throughout my career, and am delighted to have the opportunity to now be a mentor to an early career researcher through the HSRAANZ scheme.

 

Mentoring resources

Getting started with social media to promote your research

Imagine a newspaper where you get to choose the sections to include (front page, finance, politics, entertainment, sport etc), and also who writes the articles in each section…  Welcome to twitter…

Is twitter a social media fad for tween girls to share their love of Justin Bieber, or is it a social media tool that can no longer be ignored?  Twitter is now used by health officials to track disease outbreaks, and monitored for security threats in the US.  Worldwide, 21% of internet users actively use twitter each month, and over 55’s are the fastest growing demographic on twitter. So how can academics use it to promote themselves and their research?

First of all, what is twitter?  You create a profile of who you are, and then you ‘follow’ people to see what they say.  By following people who tweet about topics you’re interested in you get a twitter feed filled with information, links, news and updates tailored for you. 

For 50% of twitter users, this is all they do.  But to get the most out of twitter, you need to interact.  You can retweet things you find interesting, as well as creating your own tweets.  Tweets can be about things you hear at conferences, get by email or simply your own thoughts (see hints below on writing good tweets). You now have a global network of people who are interested in the same things you are.

For example, my profile says I’m a health economics PhD student examining costs of cancer care.  I follow organisations like ISPORorg, CHEyork, simplystats, Healtheconall and NCImedia, and people who tweet about health economics, writing and being a PhD student, like Inger Mewburn (thesiswhisperer), James Hayton (3monththesis) and Arthur Phillips (MPH_adapt). 

So why are many academics nervous about getting involved in twitter?  It seems to me to be a combination of misconceptions about the benefits available, and a fear of losing control.

Much of the public perception of twitter is that it is photos of what people ate for breakfast and the inane thoughts of music superstars.  But I conceptualise twitter as my personal newspaper.  I choose if I want to include a food section or an entertainment section in my health economics newspaper.  And if I do want some of these sections, I chose how big they are and who writes the stories that get published.  In addition, twitter also allows me to publish news that forms the content of other people’s newspapers.  And it is this aspect that can get my name, and my research, known internationally. 

The perception that academics will lose control of their content is an interesting one.  Some are worried that unpublished work, such as that presented at conferences, should not be tweeted.  But a conference is a public event, so a presenter wouldn’t present their work if they didn’t want it heard.  My perspective is that while you do potentially lose some control of who hears your message and when and where and how, the benefits of having your work seen by a potentially much larger, more diverse audience than would be in a conference session far outweighs these potential downsides.

Tips for using twitter to promote your research

  • Use your real name
  • Tweet a 70:30 mixture of professional and general interest/personal information
  • Use hashtags when you tweet , and to find people to follow
  • Be active and engaged, but  remember that you don’t have to be ‘on’ all the time
  • Everything you tweet is public and forever
  • Don’t use all 140 characters (to allow others to retweet)
  • Follow the conventions for acknowledging sources of your information
  • If something is said in public, it can be tweeted. But it might sometimes be nice to ask permission first (or let people know you’re ok with it if it is you presenting).
  • For an excellent guide to getting started with Twitter, check out the Mashable guidebook

The most re-tweeted image of all time (817,000 retweets & 300,000  favourites) 

 

 

 

Comparing the Australian and Irish Cancer Registries

Having just moved from Australia to Ireland to do a post-doc at the National Cancer Registry, I was interested in comparing the Australian and Irish cancer registration systems.  Both countries have excellent cancer registries, with some similarities as well as differences between them.  A table comparing the features of each system is below, but the primary differences are around the method of collecting data for the registry, and the amount of information captured.

In Ireland the Department of Health and Children has funded the National Cancer Registry Ireland since 1994.  Cancer registration is not mandatory.  However, data capture is close to complete through a system of active data collection through trained registry employees being stationed at hospitals around the country to collect cancer cases and data.  Most new registrations are identified through the pathology report, however public hospitals also produce lists of cancer cases discharged each year, and death notices are checked as well.  Six to twelve months after a new cancer notification, the tumour registration officer pulls the medical record for each notification, and completes the data entry.  Information is collected on the individual, the cancer and their initial treatments, with the full data list provided in the registry manual (p9) here.  Cancers are registered at the level of the individual, but are analysed at the tumour level.

In Australia, each state has an independent cancer registry, which reports a standardised minimum dataset to the National Cancer Statistics Clearinghouse at the Australian Institute for Health and Welfare (AIHW).  The New South Wales (NSW) registry, managed by the Cancer Institute NSW, is described here as an example.  Throughout Australia reporting of cancers (other than basal and squamous cell carcinomas of the skin) is mandatory, and whenever a hospital, pathology lab or radiotherapy centre deals with someone with cancer they are required by law to notify the cancer registry.  Basic demographic, cancer and doctor information is obtained and supplemented with pathology reports and death certificates; however this is less extensive than in the Irish system.  Cancers are registered at the tumour level.

Both registries produce very similar statistics such as incidence, prevalence and mortality rates, as well as specialised publications for topic areas of specific interest to the country.  Data is made available by both registries to the government and other researchers, following appropriate ethical review and de-identification.

Table 1: Features of the Irish and Australian cancer registries compared

Feature National Cancer Registry (NCR)   Ireland New South Wales (NSW) Central   Cancer Registry Australia Association of   Cancer Registries (AACR)
Funding Department of Health and Children NSW Health through Cancer Institute NSW Department of Health
Established 1994 1991.  Dataset dates back to   1972 1982
Direction provided by National Cancer Registry Board Cancer Information and Registries Advisory Committee within Cancer   Institute NSW The AACR Executive Committee advises the AIHW on the direction of the   National Cancer Statistics Clearinghouse (NCSCH) work program and the   development of publication topics and strategies, and provides technical   advice on the operation of the NCSCH.
Functions
  1.   to   identify, collect, classify, record, store and analyse information relating   to the incidence and prevalence of cancer and related tumours in Ireland
  2.   to   collect, classify, record and store information in relation to each newly   diagnosed individual cancer patient and in relation to each tumour which   occurs
  3.   to promote   and facilitate the use of the data thus collected in approved research and in   the planning and management of services;
  4.   to publish   an annual report based on the activities of the Registry;
  5.   to furnish   advice, information and assistance in relation to any aspect of such service   to the Minister.
  1.   act as a   population based register of all cancers in NSW residents
  2.   monitor   and undertake surveillance of new cases of cancer, survival and deaths in NSW
  3.   supply   timely and accurate data based on a total record of all cases diagnosed in   residents of NSW

 

 
  1.   analyse   and report on the data in its national repository of cancer incidence and   mortality statistics;
  2.   support   research based on these data; and
  3.   develop   and improve cancer statistics generally.
How are cancers registered The reporting of cancer is not mandatory, however the NCR uses active   ascertainment and follow up to ensure that there is accurate and complete   recording of all cases diagnosed. Tumour Registration Officers employed by   the registry are based at hospitals nationally.  The main source of notification of new   cases is a pathology report, however each public hospital provides a list of people   discharge with cancer which is checked against the registry, as well as   checking death notices and receiving notifications from registries in the UK. All Australian states and territories have legislation that makes the   reporting of all cancers (other than basal and squamous cell carcinomas of   the skin) mandatory. State and territory population-based cancer registries   receive information on cancer diagnoses from a variety of sources such as   hospitals, pathology laboratories, radiotherapy centres and registries of   births, deaths and marriages. When any of these institutions deal with   someone with cancer, they are required by law to notify the cancer   registries. The cancer   registry in each state or territory sends information to the National Cancer Statistics Clearing House at the AIHW to compile into a   national database of cancer incidence, the Australian Cancer Database.Cancer   data are also made available to the World Health Organization, state and local government   authorities, health care institutions, health professionals and medical   researchers.
What information is collected The medical records are retrieved 6 – 12 months after notification to   complete case information and capture relevant treatment information.  Validation checks are performed at the   point of entry and internal verifications are carried out monthly.  See page 9 of the manual (www.ncri.ie/ncri/foifiles/Manual.doc)   for details of data collected. The CCR records new cancer cases and does not capture cancer   recurrence.demographic information, brief medical details describing the cancer   and a record of at least one episode of care. The data are supplemented by   pathology reports and death certificates.
  •   name and   address
  •   sex
  •   date and   country of birth
  •   Aboriginal   or Torres Strait Islander descent
  •   clinical   details about the cancer
  •   the   notifying institution and doctor
Definition of a cancer Cancers are registered at the level of the individual, but are   analysed at the level of the cancer.  Metastasise   are associated with the primary tumour and not considered separate cancers. A case of cancer is the occurrence of a primary malignant neoplasm in   one organ of a particular person.    Therefore a case of malignant melanoma in an individual counts as one   case.  If the same person then develops   leukemia, this counts as a second case.

 

My sources, and for more information:

Cancer registration in Australia

http://www.cancerinstitute.org.au/data-and-statistics/cancer-registries/nsw-central-cancer-registry-data-access

http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/3414.0main+features782011%20%28Edition%202%29

http://www.aihw.gov.au/cancer/aacr/

Cancer registration in Ireland

http://www.ncri.ie/ncri/index.shtml

www.ncri.ie/ncri/foifiles/Manual.doc ‎

http://www.ncri.ie/pubs/pubfiles/CompletenessQuality.pdf

 

Multiple regression ‘cheat sheet’

This was a ‘cheat sheet’ I put together during the ACSPRI 2012 Winter Program course “Fundamentals of Multiple Regression” (Fun Reg). The cheat sheet simply summarises the concepts, formula’s and assumptions often used in regression analysis which were discussed in the course.

Fun Reg Cheat Sheet

This was a fantastic course that I would highly recommend to anyone looking to use regression in their research. The course description is below for your information, and you can check out the full range of courses they run at http://www.acspri.org.au/courses

Fundamentals of multiple regression: This course provides an introduction to, and the fundamentals of multiple regression, covering enough of the statistical material for the intelligent use of the technique. The approach is informal and applied rather than emphasising proofs of relevant theorems. The course begins with a review of bivariate regression and extends the relevant principles to the case of multiple regression. Particular attention is given to the application of multiple regression to substantive problems in the social sciences. By the end of the course, the student will have a knowledge of the principles of multiple regression, and the ability to conduct regression analyses, interpret the results, and to inspect elementary regression diagnostics to test the underlying model assumptions. This course provides the foundations necessary for progression to ‘Applied Multiple Regression Analysis’, and to subsequent advanced-level courses in structural equation modelling and log-linear modelling.

Resources for Emerging Researchers

This blog post was originally written for and published by the Health Services Research Association of Australia and New Zealand (HSRAANZ) Emerging Researcher Group (ERGO) section of the December 2012 Newsletter. It has been, and will continue to be, updated as I find out about new resources.

 

The number of resources for PhD students and emerging researchers available on the internet has increased exponentially in recent years.  To assist in discovering those which can be the most helpful in navigating the difficult and often confusing (but very rewarding) path to an academic career, the ERGO group (with the assistance of the PhD Group at CHERE) has put together a list of online resources.  The list is aimed at early career researchers, including PhD students, but many of the resources listed may be of interest to anyone working in health services research.

Websites / blogs

Following the blogs of people in your field can expose you to the latest research, as well as upcoming conferences, funding opportunities.  There are also a number of websites and blogs aimed specifically at PhD students and early career researchers, often with a focus on writing.

Name Summary Web / Twitter
Incidental Economist “Contemplating health care with a focus on research, an eye on reform” http://theincidentaleconomist.com/
@IncidentalEcon
Thesis Whisperer “newspaper style blog dedicated to helping research students” http://thesiswhisperer.com/
@thesiswhisperer
Healthecon-all Subscription email list which distributes messages to the international health economics community.  Set up in 1995 it has 1300 members https://www.jiscmail.ac.uk/cgi-bin/webadmin?A0=HEALTHECON-ALL
@healtheconall
Academic HE Blog UK-centric blog for news, analysis and developments in health economics http://aheblog.com/
@aheblog
PHTwitter Journal Club ‘Meets’ fortnightly to discuss selected public health related academic papers http://phtwitjc.wordpress.com/
@PHTwitJC
Simply stats 3 biostats profs post interesting ideas, article links and advice for new statisticians. http://simplystatistics.org/
@simplystats
3-month thesis “uncommon guide to thesis writing & phd life” http://3monththesis.com/
@3monththesis
AcWriMo Academic Writing Month – set a writing goal, make it public, work on it in Nov 2012 #AcWriMo

 

Twitter feeds are another good way of staying in touch with people and organizations who work in a similar area.

  • Health economics – @ScHARR – HEDS; @HERC_Oxford; #healtheconomics;
  • PhD students / early career researchers – @PhD2Published; @hildabast; #PhDchat; #Ecrchat;

 

Organisations to join

The following organizations have opportunities or resources specifically for early career researchers and/or PhD students

Name Early career researcher specific activities and resources Web / Twitter
HSRAANZ
  • Discounted student membership prices and conference registration
  • Special interest group for emerging researchers (ERGO)
  • ERGO facebook page with sharing of information, resources and opportunities
  • Mentoring program
  • ERGO specific activities at bi-annual conference (including ERGO dinner and ERGO lunchtime session)
  • ERGO seminars and workshops
  • Job opportunities advertised through mailing list
  • PhD Student prize
 www.hsraanz.org
ISPOR
  • Discounted student membership prices and conference registration
  • Research tools repository
  • Many educational opportunities (although not specific to ECR)
  • Job opportunities listing
 http://www.ispor.org/
iHEA
  • Discounted student membership prices and conference registration
  • PhD scholarships for conference attendance
 https://www.healtheconomics.org/
AHES
  • PhD scholarships for annual conference
 http://www.ahes.org.au/
ISOQOL
  • New Investigators Special Interest Group
  • New Investigators Blog
 http://newinvestigators-isoqol.blogspot.ie/

iPhone / iPad apps

These apps will all make your student / research life easier!

Name Summary
Dropbox To access all your docs from any computer, and this can include your EndNote library.  There is a special promotion at the moment if you have a student/uni email address you get an extra 3Gb storage
Endnote for iPad Access your EndNote library on the go. You will need to set up an EndNote web account, but then your articles, including PDF’s will be available anywhere, anytime.
GoodNotes To review/revise documents
EverNote For taking notes
TeamViewer To access your computer remotely
Toodledo To do list
Pomodoro Timers
  • Pomodoro Time Management Lite by rapidrabbit
  • Simple Pomodoro Timer from SourcePad
  • 30/30  – a more flexible version – you can set various time limits for a list of tasks, and it will tell you when to move on to the next one
  • http://mytomatoes.com/  –  a free online timer for desktop based pomodoros

Feedback?

Do you have an iPad app you couldn’t live without, or a blog that you really enjoy?  We would love to keep expanding and updating this list of resources, so please let us know if you have other resources that you find useful as an early career researcher.