It's interesting because I know maybe two dozen people with PhDs and every single one of them has a story about a moment of hitting rock bottom during the process and losing all hope. Obviously they all pulled through and make it out to the other side but it really doesn't sound like a pleasant experience.
It’s not even necessarily the program, but the nature of research. I liked my advisor a lot and he was helpful but at some point you need to figure out how to fly out of the nest and they can’t do it for you. During my dark period I ended up in an outpatient therapy program because my anxiety was so bad some days I wasn’t leaving my bed. My failure to figure out my problem at the time (even with a good advisor) had eradicated my self-esteem. I came in feeling a vocational calling to do research and I was just not getting anywhere with it.
The two comparisons I always made were a priest, who felt called by god to be a man of the cloth his whole life, losing his faith and the part in Moneyball where Brad Pitts character reflects on skipping college to sign a contract has a hot baseball prospect out of high school, yet being unable to succeed in the bigs.
Eventually, in conjunction with therapy and medication, I reached a critical mass of knowledge where I became, what I’d like to think at least, a good researcher. The process was just painful and I’m not sure how anyone could have made it easier.
She's got her bachelors back in 2010 and got a Masters while working full time. This part was brutal, but not technically PhD yet. She's in Health Policy, a lot of statistics and junk.
Anyway, she works for some special interest think-tank for a bit, works on insurance company some other bit, and finally settles down in the CDC where her skills in statistics / health policy were very much appreciated. She's getting to a point where it takes a Ph.D however before she can move forward with her career (she's already surrounded by Ph.Ds, and she sticks out in a bad way by not having one), so she's going for her Ph.D.
From her side of the aisle, she's seeing a whole bunch of silly 20-something year olds who don't even know what the field of Health Policy is about, trying to create Ph.D Thesis topics that have obviously no relevance to anybody in any of the fields she's ever worked in (politics, insurance, or CDC).
Meanwhile, her first idea was basically "Think of something CDC is blind at, which she can think of rather easily because she's worked there for 5+ years and everyone at the office is basically spitballing complaints about the CDC's statistics every damn day", and propose it as a Ph.D thesis.
Granted, her day-to-day work is filled with constantly interacting with Ph.Ds who are interested in improving the CDC's statistical collection techniques / improving accuracy / finding new ways to slice the data and innovation. That's literally her job. And those subjects just so happen to be very useful Ph.D thesis material for advancing the state of Health Policy.
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How much blood, sweat, and tears are we setting up Ph.D candidates for because they're straight-out-of bachelors with no real world experience or knowledge of their damn field?
Some of these things _are_ easy to figure out after you've got 5 to 10 years of real world experience.
The treadmill of Bachelors -> Masters -> Ph.D is broken. It probably needs to be Bachelors -> Real world experience -> Masters -> Real World Experience -> Ph.D.
This "Read paper -> Think of idea -> Woops, someone already did it -> Read another paper" loop from the video game, is that how most Ph.Ds try to come up with their thesis? Isn't that obviously broken compared to other "life-loops"?
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Ex: her office solved the question of "how to report statistics within one month to policy makers", because as late as 2018 or so, CDC was still on a yearly schedule of death statistics releases.
Imagine if we were still on the yearly-schedule when COVID19 happened, instead of the rapid schedule of monthly-statistics that we actually had! Monthly statistics, much like Inflation NowCasting, is actually a forecast / prediction because not all the data is in. But coming up with a forecast for this month (or last month) of data is still a problem that needed to be solved, especially in a way that policy makers would accept in a political environment where everyone's nitpicking at the details.
There's so many blind-spots and questions about how to improve statistics and statistical reporting at the Ph.D level in that field. But you are only aware of these blind-spots if you actually work in the field for a bit.
>It probably needs to be Bachelors -> Real world experience -> Masters -> Real World Experience -> Ph.D.
This was my path and my experience largely mirrored your sisters. I came to my program with a decade+ of industry work and I think that was invaluable to understanding the context of what problems are of interest. When I eventually matriculated to a position that valued PhDs, I now had a pretty concrete handle on what problems were enough of a stretch to be useful to a thesis, but not so far away as to be unrealistic. I also had a way to fund my studies without the burden of teaching and while making better pay. The younger cohort I worked with seemed to struggle because they often lacked a grounding in understanding real and feasible problems. So they were left bouncing between one half-baked idea to the next. That's what a lot of research is, of course, but it also left many to be either dropped or leave the program willingly.
I think you're right that we do a disservice to treat the bachelors >> masters >> PhD as a template to follow. There's lots of ways to skin the proverbial cat.
I know people who quit and were glad that they did so. I know people who graduated, but with the realization that research wasn't for them, and they pivoted into something else. But I honestly can't think of anyone who stuck through it and was worse off afterwards.
So you're saying you've heard of people who quit and were successful and you've heard of ones who graduated and pivoted and were successful, but not ones who graduated and weren't successful. It's almost like when you hear of someone it's more likely because they were successful in some way and the unsuccessful ones you hear less often.