Modesty seems to be under negative selection — among modern scientists, at least. So I warmed to this comment on a report of some recent work on the genetics of Africa and hunter-gatherers.

Rare genetic sequences illuminate early humans’ history in Africa

Deepti Gurdasani, a genetic epidemiologist at the Wellcome Sanger Institute in Hinxton, UK. But it’s plausible, she adds. “There is literally nothing in Africa that is not possible since we have no idea what humans were doing on the continent 5,000 years ago.”

This is from an article in Nature.

Under pressure to turn out productive lab members quickly, many PhD programmes in the biomedical sciences have shortened their courses, squeezing out opportunities for putting research into its wider context. Consequently, most PhD curricula are unlikely to nurture the big thinkers and creative problem-solvers that society needs.

That means students are taught every detail of a microbe’s life cycle but little about the life scientific. They need to be taught to recognize how errors can occur. Trainees should evaluate case studies derived from flawed real research, or use interdisciplinary detective games to find logical fallacies in the literature. Above all, students must be shown the scientific process as it is — with its limitations and potential pitfalls as well as its fun side, such as serendipitous discoveries and hilarious blunders.

And from a letter in response

My father designed stellar-inertial guidance systems for reconnaissance aircraft and, after he retired, would often present his work to physics and engineering students. When they asked him what they should study to prepare for such a career, he would reply: “Read the classics,” by which he meant Aristotle, Ralph Waldo Emerson, Jean-Jacques Rousseau and Blaise Pascal.

The best scientific and technical progress does not come out of a box. It is more likely to emerge from trying to fit wild, woolly and tangential ideas into useful societal and economic contexts.

As the historian Norman Davies once said:

“Since no one is judged competent to offer an opinion beyond their own particular mineshaft, beasts of prey have been left to prowl across the prairie unchecked.”

Or as the Economist once put it”

“…professors fixated on crawling alone the frontiers of knowledge with a magnifying glass.”

This is the tragedy of our age: 90% right and 100% wrong. And that is even before we get to  medicine.

This article and data on funding streams in higher ed is well worth exploring. It adds a necessary counterpoint to any consideration of what has happened to HE in the UK over recent decades. And, I see the time span maps closely to my own career as a Professor. I still struggle with the ‘why’ question. Some of the graphs are scary.

This is a THE quote referring to the late Sir David Watson. [Link]

In England, he said, undergraduates had been reduced to “state-sponsored Wonga-style customers”; you can see what I mean about that turn of phrase.

I can indeed. And I claim to have come up with  similar terminology independently.

I do not have a coherent overview of many of the traditional professions, but I wonder if people will soon say similar things about doctors.[Link]

“The big issue that concerns me at the moment in the English education system is the supply of high-quality teachers. We’ve seen quality issues in recruitment to teaching and our schools are getting increasingly desperate to find decent teachers. The whole workload issue has come to a big head again in England with teachers having very big workloads and their conditions of service is deteriorating a lot recently. We’re seeing a big exodus in teaching and so of course, we need a bigger inflow to maintain the balance.”

Michael Day.

Wiliam is talking about schooling, but it is also true of medical education.[Link]. 

“For me, I think the issue in the United States in particular is how we improve education at scale. I argue there are two things that have particularly powerful impact. One is a knowledge-based curriculum, recognizing that the purpose of curriculum is to build long-term memory into our students and what distinguishes novices from experts is knowledge not skills. And the second one is creating a culture where every teacher accepts the need to improve, not because they’re not good enough, but because they can be even better.”

I am pleased with the  comment about long-term memory: intellect’s ballast. Knowing things matters. 

“They have mobile phones, social media, but no proper toilets and clean water.” Link.

This was the ‘final’ video in the edderm101:core concepts video series (link to series).

 

Skin immunology (KISS!) from jonathan rees on Vimeo.

Maybe more of a theory than a law, but still:

Any eLearning tool, no matter how openly designed, will eventually become indistinguishable from a Learning Management System once a threshold of supported use-cases has been reached.

They start out small and open. Then, as more people adopt them and the tool is extended to meet the additional requirements of the growing community of users, eventually things like access management and digital rights start getting integrated. Boil the frog. Boom. LMS.

Norman’s Law of eLearning Tool Convergence – D’Arcy Norman dot net

Big Data and Predictive Analytics: Recalibrating Expectations | Clinical Decision Support | JAMA | JAMA Network

Yet despite these innovations and those to come, quantitative risk prediction in medicine has been available for several decades, based on more classical statistical learning from more structured data sources. Despite reports that risk models outperform physicians in prognostic accuracy, application in actual clinical practice remains limited.

It seems unlikely that incremental improvements in discriminative performance of the kind typically demonstrated in machine learning research will ultimately drive a major shift in clinical care. In this Viewpoint, we describe 4 major barriers to useful risk prediction that may not be easily overcome by new methods in machine learning and, in some instances, may be more difficult to overcome in the era of big data.

The hype cycle marches on.