I’m beginning to build a bank of resources to support people to use the measures of perceived control over mortality risk that Daniel Nettle and I developed some years ago (Pepper & Nettle, 2014). So far, these measures seem to be a good predictor of health behaviour and, in our data, they outperform the Multidemsional Health Locus of Control (MHLC), which is a commonly used measure examining a similar construct.
To get things started, I’ve created my first ever #BetterPoster (see here for more on the “Better Poster” concept) that gives a brief overview of the theory and evidence regarding the relationship between perceived uncontrollable mortality risk and health behaviour. I’m presenting this poster at EHBEA2021, and Richard Brown will be giving a talk on some of the evidence summarised in panel 4 of the poster below. The 2-minute audio recording that accompanies the poster can be downloaded here.
I’ve also created a 1-page guide to using the measure, which gives the question text and an at-a-glance summary of what the responses represent, with references for further details. You can download the guide here.
The poster above is embedded as an image, so the links don’t work. Here are the links to the key references:
Although I understand that many people are suffering from “Zoom fatigue”, an online conference offers a number of fresh advantages. It becomes easier for people from all over the world to participate without time, cost, or carbon footprint concerns becoming barriers. We can be innovative about our scheduling too. Having some pre-recorded talks and posters available in advance of the conference will mean more time to interact with each other on the day. More interaction can mean more ideas, more fun, and more potential for collaboration. Another advantage of having some pre-recorded talks: you can pause, rewind, and watch again! No more wondering if you’re asking a silly question because you didn’t quite hear something that was said earlier on in the talk. Equally, if the topic of the talk isn’t quite as you expected, you can stop watching without fear of disrupting others in the audience. This year’s meeting will enjoy all these advantages, plus some of the buzz of a live event with some live talks and Q&A sessions.
To really boost the interactivity of the conference, we’ll also be running our first ever Evolutionary Medicine and Public Health Grand Challenges! Conference delegates can sign up to work in virtual teams to address the big questions and challenges facing medicine and public health today, with topics ranging from ageing to tuberculosis. The aim of these events is to encourage new connections and collaborations, and to spark innovation in the EMPH community. Check out the ISEMPH-2021 website for further details: https://isemph.org/Grand-Challenges-2021
A little while ago, I was contacted by someone who was looking for a shape file for the Newcastle upon Tyne area, as they wanted to map some data as part of a research project.
It occurred to me that other people might be looking for such a file since, back when I made my maps of transport usage in Newcastle (see previous blog), I’d had to source an ONS shape file of all the LSOAs in England and then manually edit it down to only those contained within the Newcastle area. Should you wish to avoid doing all that work yourself, here is the file!
In one of my previous posts, I presented some interactive maps, made using Google Fusion Tables, to support a paper on Cross-country relationships between life expectancy, intertemporal choice and age at first birth, written with my collaborator, Adam Bulley. However, Google have since … Continue reading →
In the process of producing a recent meta-analysis on telomeres as markers of exposure to stress and adversity, we needed to convert various effect types (e.g. standardised betas, F-ratios and T-statistics) to correlation coefficients, ready to be meta-analysed. The script is fairly straightforward, but it took a little while to write the script and format the data entry to work smoothly with the script. So, to save others some time and energy, I’ve made a generic version of the script, along with a template input file–just to make things easy to use. You can download the files here, along with a guide on how to correctly enter your data into the template input file, so that the correlation converter script can read it.
1. Enter your data into this template csv, using the table in this document as a guide.
2. Make sure the template csv is in the same folder as this R script.
3. Run the script in R. If you don’t have R, you can learn more and download it here.
4. Once you’ve run the script, a new file, entitled “MetaAnalyisCommonAssociations.csv” should appear in the same folder. It should contain all the data in your original csv, plus two new columns containing the “CommonEffect” (the correlation coefficient) and the “CommonEffectVariance” (the measure of variance).
What is the script doing? The script contains comments explaining what each piece of code does, but for those who find diagrams and equations easier to follow, this flow chart shows the operations it performs: