My take (as a paying subscriber to both, largely because I want to support this world and think both teams are insanely talented and great) is that they're solving similar problems, with some different features.
I wish I could use both with a sync between them until I figure out which one I'd want to stick with. Or stick with both.
It's absolutely declined, no question. Further, the advanced search tools have been all but eviscerated, so it's even more difficult to find what you want even if you know what you're looking for.
I think this is a great summary of some of the main challenges nurses are facing.
I'd add to #1 that travel (temp) nurses are making 4x+ more than staff nurses, I've heard as high as $13-17k per week in high-demand areas. This exacerbates the problem, as staff nurses hear this, and if they can, they leave. Travel nurses can be great, but they won't know the facility and workflows and people as well as staff nurses: staff nurses now pick up more slack, all while getting paid 1/10th what their new colleagues are. This is more than most doctors.
For #3, this problem is made worse by additional compliance burden. Nurses need to document more and more, click more and more, read more and more… with less and less time. And on systems that are unpleasant to use. Among other issues, this leads to problems like these[0], which drive more and more nurses away.
I'm working with a badass team on solving some parts of these problems, particularly relating to technology and workflows. If you're interested (across basically any role, but product designers, engineers, product managers are top of mind right now), let me know (email in bio)!
Same, I have a free account from having signed up when it was something like $8 for life, the core features actually seem to work pretty well (though I have had some documented search issues where tags weren't being searched; I think that was resolved and one-off).
I pay for the archiving service partially to support Pinboard because I want it to stay solvent, partially because it soothes me to know I can refer back to websites (at least the page I archives) that I found valuable in the past.
I loved Pinboard, too. (past tense) I was an early adopter and paid for lifetime access - an option that was available at the time. Now, the API is often down so my native clients I use on macOS and iOS are useless.
Sadly, I've moved on. Espial seems to fit what I need.
This book is next on my reading list. One question that's come to mind before having read the book is if Singapore is an example of a highly-legible planned state's success?
I wouldn't say so. Scale matters. What worked for Singapore, may not work for China.
There is this great article about the story of Singapore[0], it was also discussed on HN some time ago. I believe one of its main takes really resonates with "Seeing like a State" thesis.
Decision-makers must rely on simplified models to make their decisions. All schemata are by nature imperfect representations of reality. Indeed, a scheme that reflected reality perfectly would be cluttered and uninterpretable. The reality is always more complex than the plan. In large countries, the planner is further from ground reality than in tiny city-states. Abstractions and errors inevitably compound as the distance increases
Ironically, Lee Kuan Yew himself had no patience for other people’s models. In his words, “I am not following any prescription given to me by any theoretician on democracy or whatever. I work from first principles: what will get me there?” If there is a lesson from Singapore’s development it is this: forget grand ideologies and others’ models. There is no replacement for experimentation, independent thought, and ruthless pragmatism.
>There is no replacement for experimentation, independent thought, and ruthless pragmatism.
I find this quite interesting. As a programmer I find you can only get to the real requirements by experimenting and going back and forth with the customer.
Applied to politics it would be really helpful if we could easily experiment "in the small" and then incrementally scale what works. However democratic processes, at least in my country, are so slow that most people go for the "go big or go home" approach.
It would therefore be helfpul to have incremental laws where you say start the implementation at city level, maybe in a few test cities, if that seems favorable automatically scale to a few states, and if that still works scale to the whole country.
In a complex economy you need these small "tests" to maybe patch issues before scaling it to the whole country. And you would avoid costly mistakes, trying things that sound good on paper but eventually don't work out well.
Singapore and Hong Kong are exceptions to general trends. Being a small city state/port city gives you the ability to do things that larger states aren’t able to do (similar to banking havens in Europe like Luxembourg and Lichtenstein).
That's a beautiful question. I am not intimately familiar with Singapore's planning processes. The authors above (Jane Jacobs, James C. Scott) would argue that the best cities/countries are ones that have a centralized strategy that leaves enough leeway to enable each community to optimize their specific situation on their own.
I don't know if Singapore does that or not. Do you have a POV?
I've been meaning to read Lee Kuan Yew's "From Third World to First" to learn more but haven't found the time.
Singapore does have the advantage of being compact and having the ability to set policy at all levels at once. The US can't do that, because cities and towns depend on the state and federal governments for funding, but those same cities and towns have some autonomy in how they run day-to-day governance.
Thank you. Analyzing this kind of data is the kind of thing I'm talking about to try and get to grips with the risk-management calculation involved.
But even if there's no medical flaws with this study, it doesn't necessarily answer the question to me because this overall point might really be more of a psychological question mark than a medical question.
If you tell people they had Covid, some portion of people who have been stressed out by the media focus may panic and mentally exaggerate post-viral symptoms based solely on the fear they feel with Covid over the flu, even if the actual medical conditions they experienced would not cause them to panic if they were told they had the flu.
I think a study that would actually illuminate here is to tell half the patients who had Covid that they actually had the flu, and telling half the patients that had the flu that they actually had Covid, and then doing a comparison on how peoples' perception of which illness they had impacted how frequently they reported symptoms. (I don't know, but I doubt that would be considered medically ethical though.) My guess is that the media focus on Covid is impacting how people choose to go to the doctor regarding post-viral symptoms and there's no real way to measure this without some unusual experimental design.
I don't have solid experimental data on this. But my own experience was that I went into the pandemic assuming that I was young (I'm 28) and that covid likely wouldn't affect me too badly, I then catch it quite early on (Apr 2020), and come out of the initial infection finding that exercise causes me heart pain and fatigue to the point that I sometimes actually fall asleep immediately afterwards (this has gradually improved over time, but is still quite restrictive on activities that I am able to do). And now almost 2 years later this is still affecting me. I wasn't expecting this at all. I was expecting to be able to continue with my life as usual.
I don't know about you, but I've never heard of a young person getting these kind of symptoms from flu. Other viruses like Glandular Fever are known to cause these kind of affects of course. Based on this I rather suspect that the reason there is more media focus on long-covid than long-flu is because covid is causing a lot more post-viral symptoms than flu does. That doesn't mean there is no media focus factor, but I don't think it's the main driver.
I think the question is: in the absence of solid experimental evidence why would you assume that the effect is psychological when post-viral effects are well documented across multiple viruses, and have been confirmed by clinical diagnoses by Doctors in thousands of covid cases.
Being skeptical until proven unequivocally is a good approach to scientific enquiry, but it is not good public policy where we must take decisions and act despite uncertainty.
I don't have objective proof of this in the sense of "2+2=4", but I think what I've been talking about in many previous posts is a logical systemic explanation and has been repeatedly observed in the last few years.
The media's business model has changed.
At one point the media's business model was based on trust. People watched a guy like Dan Rather repeatedly because they thought he seemed genuine about telling the truth. I'm sure they knew he'd have his own feelings and human biases, but they came back to him because they thought he was focused on truth and felt trustworthy.
The media's business model has changed because of technology such as the ability to measure clicks, measure the intensity of emotion, and social-media and search algorithms that promotes what gets the most activity. Once the media has the ability to essentially drive their own profit via algorithms promoting the emotionally most intense news stories and ideas that gets people sharing and watching, the fear-porn or outrage-porn of the current news cycle will become the only reality for many people.
As an example, many people went from being overly scared about Omicron and Covid-19 fear a few weeks ago, to supreme outrage about Putin and Ukraine and making that the central part of their identity without skipping a beat. And I have to note that almost nobody gave half a shit about past Russian invasions of Ukraine or Georgia until the media told them that they needed to care about this. (Not suggesting that people shouldn't care about this, but the intensity of this switch and suddenly caring about this part of the world feels notably shaped by the media promoting it this time.)
Outrage or fear porn is the new ruler of reality. The media has supreme influence in shaping peoples' perceptions, and if a study about Covid after-affects is done, I'm sure that peoples' fears that are magnified by this new media business model will have some impact in how people report symptoms. If you tell people that Covid is super-scary and long-covid is the new big concern, at least some people will feel panic and be more likely to go to the doctor and express concern over identical symptoms that they otherwise might have not cared about if they experienced it in say 2014 with a flu.
I get where you're coming from, and agree with you on the effects of media focus.
But I don't think that's a reason not to be worried about this. Reading various threads here, there are people describing pretty awful post-COVID conditions, some that seem way more severe than anything associated with post-flu conditions.
Yes, these are anecdotes. But if we believe them to be true, they should still worry us, even if the incidence is less than 1%. Because if there is even a 0.1% (or maybe even 0.01%)[0] chance that, after recovering from COVID, I might end up physically debilitated for months or years, I will absolutely change my behavior to make it less likely that I get COVID in the first place, regardless of loosening of masking and distancing restrictions. Some might consider that an overreaction, but that's my choice to make.
As you and others point out in this thread, it's not just incidence that matters; severity is important too. If I have a 0.1% chance of dying doing a particular optional activity, I would not do that activity. If instead I have a 0.1% chance of a minor injury doing that activity, I would probably still do it if it was something I believe I'd enjoy.
[0] Just to give you an idea of my own personal risk tolerance: I just did a quick search on fatalities from skydiving, and it looks like it's around 0.0002% (tandem, not solo, based on 2019 reports). That's pretty low, but still high enough for me to not be particularly interested in doing it, even though I think it would be fun. Granted, this is a very different situation than trying to avoid getting COVID.
> Yes, these are anecdotes. But if we believe them to be true, they should still worry us, even if the incidence is less than 1%. Because if there is even a 0.1% (or maybe even 0.01%)[0] chance that, after recovering from COVID, I might end up physically debilitated for months or years, I will absolutely change my behavior to make it less likely that I get COVID in the first place, regardless of loosening of masking and distancing restrictions. Some might consider that an overreaction, but that's my choice to make.
You and I have somewhat different thought-processes when it comes to thinking about risk. All other things being equal, I wouldn't want to risk even a 0.001% chance of significant problems either: but the deciding factor for me is valuing my freedom and not wanting to be scared for the rest of my life. Also, there's no guarantee that you can still avoid Covid anyway even if you do 100% of things perfectly anyway.
That said, I respect everybody's right to choose. That's one value that I hope everybody can learn to respect again.
That paper is better than most in the space, in that it has an actual control group, and there are not huge health/age differences between the populations at baseline. Nonetheless, a few interesting things about that paper that stand out to me:
1) Depression and anxiety are the most common "long covid" symptoms, by far. Even "abnormal breathing" doesn't linger to nearly the same extent (fig 1). In fact, when you look at the co-variate matrices, depression and anxiety stand out as a brightly colored axis for the "long covid" cohort (but not for flu), indicating that many/most "long covid" patients had correlated problems with depression and anxiety that aren't seen in the "long flu" group (fig 3).
2) If you look at the last figure (fig 5), it's obvious that the "long covid" symptoms are enriched in the oldest and sickest patients -- yet the main text doesn't break out these groups explicitly, and instead presents "long covid" symptoms as something equally likely to affect all. That's clearly not true. The authors could easily have done this, but did not.
3) The "long covid" symptoms include the primary disease itself (measurements start on day 1), and drops off by 3 months after diagnosis (fig 1), but the authors try to distract from this by emphasizing the group that develops symptoms at any point in the six months after diagnosis, even if they don't have the corresponding symptom in the first 90 days after infection. In fact, 40% of the people in the "symptoms within six months" cohort do not have symptoms in the first 90 days! The authors try to claim that this is proof of some kind of evolving "network" of symptoms...but ignore the simpler explanation that they're detecting "symptoms" unrelated to the original illness.
Long covid papers often make the mistake of blurring together severe, long-term symptoms with minor or unrelated ones, and mixing old people with young people. The authors here have done little to prevent either class of error. Also, looking at this, you also have to conclude that the "long covid" cohort cannot easily be separated from a group of people struggling with depression and anxiety. Are the symptoms caused by the depression? With symptoms like "fatigue" and "pain", it's quite probable.
> With symptoms like "fatigue" and "pain", it's quite probable.
I'd disagree with this. I'd argue that the causation is equally likely to go the other way. Fatigue is a well-documented symptom of multiple viral infections. And painful auto-immune induced symptoms are well-documented in post-covid patients. And who wouldn't be depressed if they can no longer work or do the activities they usually enjoy because they can't get through a day without running out of energy while they can see everyone else getting back to their normal lives.
Everything in the paper is a correlation, and correlations alone do not imply a causative relationship. Therefore, you can just as plausibly argue that the symptoms cited have nothing to do with covid. That said, there's no reason to believe that people with "long flu" (the control group) would be less likely to experience depression as a result of their lingering symptoms, but that is what is seen. And remember that depression and anxiety are more common than the other symptoms -- they're happening in the absence of other "long covid" symptoms in this cohort.
Does Covid cause depression and anxiety, or are depressed and anxious people more likely show up to the doctor seeking covid treatment? You can't tell from a study like this, but the latter is a simpler explanation, consistent with a radical, society-wide initiative that uprooted people's social structures during the study period.
Also, this:
> painful auto-immune induced symptoms are well-documented in post-covid patients.
Is not true. There is some speculation and self-reporting of symptoms, but it has not been "well documented", or documented at all.
I think the categories and the attributes assigned to them are fuzzy. That said fatigue that is attributed to CFS/ME is not just being tired, it's a whole other level, it is a crushing fatigue. CFS/ME fatigue also has clearly definable characteristics; for example Post-exertional malaise (PEM) that does not occur in depressed people unless they also have CFS/ME. In which case they're probably miscategorized.
