The Sorrows of Psychiatry(nature.com)
nature.com
The Sorrows of Psychiatry
https://www.nature.com/articles/d41586-019-01170-1
55 comments
Well, my depression went away when I cut out plants and sugar. Not much more to say.
You cut out all plants? Like all fruits/veggies? Wheat? etc?
Somebodies bowel movements are set to hard mode.
You are now solely a carnivore?
yes, about a year now.
Depression, Anxiety, joint pain, and back pain went away.
White spots in nails are almost gone completely, and my hair is much fuller now than it was before.
[deleted]
Some years ago I was told that neurology had started to quietly replace psychiatry, overwriting its poor reproducibility and conjectures with testable, reproducible, and consistent chemical and physical explanations. I hope it continues.
I have heard very little in the way of hard sciences finding more robust ways to diagnose or treat mental illnesses. Source?
I don't know why you are being downvoted. The famed neuroscientist ramanachandram had a story about how psychiastrists were assigning woman with all kinds of disparate diagnoses for her occasional erratic behavior. Turns out neuroscientists found out she was having small strokes and once they fixed it with brain surgery, her erratic behavior went away.
Psychiatry is like fixing a software bug by telling the customer to simply avoid doing things that causes the bug rather than correcting the actual code and fixing the software.
I know some people don't like reductionist reasoning, but the more I think about it, the more I'm inclined to believe that all psychological problems are ultimately a brain problem. We just don't know enough about the brain yet to fix many of those problems.
Psychiatry is like fixing a software bug by telling the customer to simply avoid doing things that causes the bug rather than correcting the actual code and fixing the software.
I know some people don't like reductionist reasoning, but the more I think about it, the more I'm inclined to believe that all psychological problems are ultimately a brain problem. We just don't know enough about the brain yet to fix many of those problems.
I would guess it's because it is an huge simplification of the relationship between the two disciplines. The fields of science are not discrete.
I’m not sure if all psychological problems are brain problems, at least not at any level that we can interact with at a meaningful level yet.
Example: let’s say you had a moderately abusive parent. The result is low self esteem but nothing diagnostic worthy. While I’m sure there’s a concrete neurological effect that might be measurable, but I seriously doubt that neuroscientists would have the tools today to interdict and make a difference in the way that psychology might.
Now I do agree that neurology is replacing psychology, I just don’t think it has replaced it yet.
Example: let’s say you had a moderately abusive parent. The result is low self esteem but nothing diagnostic worthy. While I’m sure there’s a concrete neurological effect that might be measurable, but I seriously doubt that neuroscientists would have the tools today to interdict and make a difference in the way that psychology might.
Now I do agree that neurology is replacing psychology, I just don’t think it has replaced it yet.
You're right. Unfortunately, it's the conditions that lie in the realm of conjecture and opinion that are the province of psychiatry. Sometimes the conjecture is good, sometimes it's not.
When the actual biological cause for a condition is found, it moves over to being treated by other medical specialties and loses its psychiatric significance. Parkinson's, now treated by neurologists is a one such example. Hepatic encephalopathy from liver failure, which can cause dramatic personality changes, is another.
Sometimes its changing social conditions that help recategorize a diagnosis. Case in point, the vote to remove homosexuality as a psychiatric diagnosis from the DSM in 1973.
Psychiatry is the catch-all until we actually figure out how to fix something. You can extrapolate this: if something is a psychiatric diagnosis, theories as to cause and treatment are actually hypotheses, sometimes uncomfortably out of date. If the treatment works for you, that's excellent. But if it doesn't, realize you're dealing with a bug in the most complex object in the universe. Hitting it with a hammer might not work.
When the actual biological cause for a condition is found, it moves over to being treated by other medical specialties and loses its psychiatric significance. Parkinson's, now treated by neurologists is a one such example. Hepatic encephalopathy from liver failure, which can cause dramatic personality changes, is another.
Sometimes its changing social conditions that help recategorize a diagnosis. Case in point, the vote to remove homosexuality as a psychiatric diagnosis from the DSM in 1973.
Psychiatry is the catch-all until we actually figure out how to fix something. You can extrapolate this: if something is a psychiatric diagnosis, theories as to cause and treatment are actually hypotheses, sometimes uncomfortably out of date. If the treatment works for you, that's excellent. But if it doesn't, realize you're dealing with a bug in the most complex object in the universe. Hitting it with a hammer might not work.
[deleted](3)
>chlorpromazine... enabled the demonstration that biochemicals such as neurotransmitters are involved in mental disorders
It does no such thing. This drug, and most antipsychotics, simply shuts down the forebrain. It "treats" psychosis by shutting off your mental function. As soon as you remove the drug (go "off your meds" because you want to think and feel again), psychosis resumes. This tells us nothing about the nature of psychosis.
It does no such thing. This drug, and most antipsychotics, simply shuts down the forebrain. It "treats" psychosis by shutting off your mental function. As soon as you remove the drug (go "off your meds" because you want to think and feel again), psychosis resumes. This tells us nothing about the nature of psychosis.
It seems to me that antipsychotics are too often used as medicinal straitjackets. They are indispensable in stopping acute psychosis, but they don't treat anything.
They are rightly called major tranquilizers, "antipsychotics" is relatively new marketing that oversells them as a cure rather than a bandaid.
https://en.wikipedia.org/wiki/Antipsychotic
https://en.wikipedia.org/wiki/Antipsychotic
From recently discovering Dr. Sarno’s work on physical pain (Eg. Back pain). My current working theory is most psychological pathology comes from repressed rage. It sounds crazy but thousands of people have been cured from chronic back pain, depression, IBS, the list goes on from just learning to get in touch with their embarrassing and painful feelings of rage. Most people have no idea how much bottled up rage is living in their subconscious.
“Though he may not be a household name, Sarno is probably America’s most famous back pain doctor. Before his death on June 22 2017, a day shy of his 94th birthday, he published four books and built a cult-like following of thousands of patients — including Howard Stern and Larry David. Many of them claim to have been healed by Sarno, who essentially argued back pain was all in people’s heads. And Sarno himself often said that some 80 percent of his patients got better.”
“After digging a little deeper, I learned that some of Sarno’s theories are now even being validated by science — specifically, that there can sometimes be an emotional basis for chronic back pain. And that’s an important truth mainstream pain medicine still hasn’t quite figured out what to do with.”
“More specifically, he believed that the brain distracts us from experiencing negative emotions by creating pain. We may not want to accept the uncomfortable truths that we are angry with our children, or that we hate our job, so instead of thinking those thoughts, we focus on the pain.
He also thought that pain was created by reduced oxygen and blood flow to the muscles and nerves of the body. So our brains unconsciously [sub consciously] direct blood away from certain areas of our body, and that creates pain.” - https://www.google.com/amp/s/www.vox.com/platform/amp/scienc...
The problem is that Sarno was at his peak when western medicine wasn’t willing to look at studies that show that emotions can be the root cause of physical conditions. Which is crazy because the placebo effect is well documented. Thus there is obvious evidence that our mind has the ability to effect our body in various ways (including our immune system).
“Though he may not be a household name, Sarno is probably America’s most famous back pain doctor. Before his death on June 22 2017, a day shy of his 94th birthday, he published four books and built a cult-like following of thousands of patients — including Howard Stern and Larry David. Many of them claim to have been healed by Sarno, who essentially argued back pain was all in people’s heads. And Sarno himself often said that some 80 percent of his patients got better.”
“After digging a little deeper, I learned that some of Sarno’s theories are now even being validated by science — specifically, that there can sometimes be an emotional basis for chronic back pain. And that’s an important truth mainstream pain medicine still hasn’t quite figured out what to do with.”
“More specifically, he believed that the brain distracts us from experiencing negative emotions by creating pain. We may not want to accept the uncomfortable truths that we are angry with our children, or that we hate our job, so instead of thinking those thoughts, we focus on the pain.
He also thought that pain was created by reduced oxygen and blood flow to the muscles and nerves of the body. So our brains unconsciously [sub consciously] direct blood away from certain areas of our body, and that creates pain.” - https://www.google.com/amp/s/www.vox.com/platform/amp/scienc...
The problem is that Sarno was at his peak when western medicine wasn’t willing to look at studies that show that emotions can be the root cause of physical conditions. Which is crazy because the placebo effect is well documented. Thus there is obvious evidence that our mind has the ability to effect our body in various ways (including our immune system).
Howard Stern described him as, "the Steve Jobs of pain management."
https://www.youtube.com/watch?v=xEBI7bwrRO8
https://www.youtube.com/watch?v=xEBI7bwrRO8
If one googles:
serotonin, pain, phantom
...you'll find a fair amount of literature on serotonin as playing a key role in the experience of pain, in some cases seeming to prolonging the perception of pain where the underlying physical damage has completely healed.
serotonin, pain, phantom
...you'll find a fair amount of literature on serotonin as playing a key role in the experience of pain, in some cases seeming to prolonging the perception of pain where the underlying physical damage has completely healed.
Funny you mention rage. I often say that I had to overcome a very bleak period in life. And I found screaming or even proto-screaming (no need to make a sound but think about raging out as a way to shake pain, drag, fatigue out of your mind and refocus). It often ~works enough.
And sorry for the ultra fuzzy anecdote
And sorry for the ultra fuzzy anecdote
Funnily enough, psychiatry seems to function a lot like our field, based on trends, because ultimately the brain is a black box. You can't see how it works, you can only feed it input and observe the output, and if it's consistent enough you can record it as an observation.
Apart from the truly severe conditions that prevent individuals from even participating in society, IMHO what psychiatry seems to fail to account for is context. For example, it is said that many people suffer from depression, but instead of looking into the core causes, they would rather just pump patients with meds and hope it gets better with time. Often times though, all that happens is that the patient builds tolerance to medication, so larger and larger doses are needed. It makes me think: what if those people cannot escape their condition to begin with? Can't even know if you don't at least try.
Apart from the truly severe conditions that prevent individuals from even participating in society, IMHO what psychiatry seems to fail to account for is context. For example, it is said that many people suffer from depression, but instead of looking into the core causes, they would rather just pump patients with meds and hope it gets better with time. Often times though, all that happens is that the patient builds tolerance to medication, so larger and larger doses are needed. It makes me think: what if those people cannot escape their condition to begin with? Can't even know if you don't at least try.
The first and biggest mistake people make with psychiatry is that it will somehow fix anything, or do anything not relating to the patient. You're absolutely right. There are environmental reasons for phobias, PTSS, Autism, Traumas, ...
So if you're being abused, there's absolutely nothing that psychiatry can do for you, because they can do nothing about the abuse (other than taking you in and throwing you back into the abuse after 2 months). Same with ancillary fields: social workers will NEVER do anything about that teacher that's abusing your kid, they will only "treat" the child (maybe with force, maybe with internment, maybe against the will of both child and parent, but only the child).
Likewise, many issues are caused by poverty, or other effectively environmental factors that just won't change with psychiatric treatment. There is absolutely nothing that can be done.
You're also forgetting, with those medications, what the patients will build tolerance for and how that happens. Opiates, Xanax, "Benzos" and SSRIs all have different mechanisms, but it boils down to the following: you will NOT like the long term effects. You can responsibly take these medications for periods from hours to a few weeks (to end what they call a manic episode or a huge panic). More will have permanent consequences. Eventually you will become permanently depressed, to the point that "zombie" will be a word used to refer to you. The way your body adapts is by raising the threshold of dopamine it takes to reward you, to a point that no non-medication-induced (and eventually any non-overdose amount) will make you happy. You won't get out of bed, you won't learn, go for a walk, wash yourself, ... hell Miss/Mister World could walk into your bedroom, offer anything goes sex, and you still wouldn't be able to find the motivation to do anything. Once that threshold is raised to a point that only medication can provide rewards, you're not coming back anymore.
So if you're being abused, there's absolutely nothing that psychiatry can do for you, because they can do nothing about the abuse (other than taking you in and throwing you back into the abuse after 2 months). Same with ancillary fields: social workers will NEVER do anything about that teacher that's abusing your kid, they will only "treat" the child (maybe with force, maybe with internment, maybe against the will of both child and parent, but only the child).
Likewise, many issues are caused by poverty, or other effectively environmental factors that just won't change with psychiatric treatment. There is absolutely nothing that can be done.
You're also forgetting, with those medications, what the patients will build tolerance for and how that happens. Opiates, Xanax, "Benzos" and SSRIs all have different mechanisms, but it boils down to the following: you will NOT like the long term effects. You can responsibly take these medications for periods from hours to a few weeks (to end what they call a manic episode or a huge panic). More will have permanent consequences. Eventually you will become permanently depressed, to the point that "zombie" will be a word used to refer to you. The way your body adapts is by raising the threshold of dopamine it takes to reward you, to a point that no non-medication-induced (and eventually any non-overdose amount) will make you happy. You won't get out of bed, you won't learn, go for a walk, wash yourself, ... hell Miss/Mister World could walk into your bedroom, offer anything goes sex, and you still wouldn't be able to find the motivation to do anything. Once that threshold is raised to a point that only medication can provide rewards, you're not coming back anymore.
Interesting addendum. Thank you.
Are there any statistics or studies regarding the becoming of these zombies? I thought that the body, if long enough under such medication, becomes incapable of producing its own dopamine, not that the threshold becomes higher.
Are there any statistics or studies regarding the becoming of these zombies? I thought that the body, if long enough under such medication, becomes incapable of producing its own dopamine, not that the threshold becomes higher.
Well, I've also read about chronic depression causing brain damage and in later stages it gets harder to distinguish depression from dementia.
As we learn more about neural networks, I predict that we'll hit new advances in psychology and psychiatry.
If one oversimplifies neural networks and thinks of them as pattern-matching machines, it would make sense that our brain's neural network would have trouble if they were fed a lifetime of bad training data (abusive childhood/relationships) and therefore had bad/nonconstructive reactions to normal stimuli.
If one oversimplifies neural networks and thinks of them as pattern-matching machines, it would make sense that our brain's neural network would have trouble if they were fed a lifetime of bad training data (abusive childhood/relationships) and therefore had bad/nonconstructive reactions to normal stimuli.
You need to visit a psychology department, or worse, a social sciences department, and then visit a computer sciences department. This will never happen, or at least, not without a few generations passing at minimum.
For the same reason that people who have changed a tire on a bike cannot repair a jumbojet. The statistics accepted in psychological papers ...
https://en.wikipedia.org/wiki/Replication_crisis
Or, more dramatically:
https://phys.org/news/2018-10-real-fake-hoodwinks-journals.h...
For the same reason that people who have changed a tire on a bike cannot repair a jumbojet. The statistics accepted in psychological papers ...
https://en.wikipedia.org/wiki/Replication_crisis
Or, more dramatically:
https://phys.org/news/2018-10-real-fake-hoodwinks-journals.h...
You seem to be saying that the replication crisis is limited to social sciences and pscyhology, when in fact it's common across all of medicine and a lot of other science.
If you're saying "most science is bullshit" you can point to the replication crisis. If you're saying "only psychology is bullshit", well, that shows your bias pretty clearly.
If you're saying "most science is bullshit" you can point to the replication crisis. If you're saying "only psychology is bullshit", well, that shows your bias pretty clearly.
Neural net research also has problems with replicability.
Not knowing machine learning - are neural nets deterministic at all? Given all their randomness, I'd assume not.
The vast majority are explicitly not deterministic. Starting with random initialization, and not ending there (explicitly introducing noise, using different (and very low) levels of accuracy, random choices for updates, ...)
It is often explicitly mentioned in articles, currently mostly for reinforcement learning (or it might be that that's just the vast majority of papers I see these days), that, for example "this network converges about 3 times out of every 10 attempts". Often with graphs leaving a selection of the failed attempts.
This is very different from the explicit lying you find in psychology papers and theories.
It is often explicitly mentioned in articles, currently mostly for reinforcement learning (or it might be that that's just the vast majority of papers I see these days), that, for example "this network converges about 3 times out of every 10 attempts". Often with graphs leaving a selection of the failed attempts.
This is very different from the explicit lying you find in psychology papers and theories.
> are neural nets deterministic at all? Given all their randomness, I'd assume not.
That's actually an interesting question; I'm probably not the person to answer it, since my experience with NNs is admittedly on the "still learning" side of things, and that learning has not been via any credentialed sources...but I'm willing to take a stab at it.
NN models - that is, the thing that results from training a neural network, are typically "fixed", in that once trained, the model doesn't change - at least classically. I imagine that somewhere out there, there may be NN models which can change as data they are processing is run through them, learning on the fly so to speak. I'd have to research it; I'm sure it's something that's done or been done?
But normally, once a NN has been trained, it's model is "fixed" and doesn't change when inputs are presented to it. The training phase is - or appears to be - fairly stochastic. I'm not sure I'd want to call it random, though, because I don't recall any kind of random numbers being used during back-propagation.
But the data that is presented to the neural network is usually randomized in presentation - and sometimes content. That is, say you're training the NN on recognizing horses. You might have several thousand images of horses, but you don't want to show just those. You might want to generate many more - rotate each one just a bit, skew it just a smidge, maybe change the color and/or contrast/brightness, etc - to in effect generate a bunch more positive (and just as many negative - so you don't get bias or overfitting happening) examples to train the NN on what is a "horse" vs what is a "not horse".
So that data is somewhat "random" - but from what I recall, the actual mechanics of training - the algorithms of forward passes and backwards passes (backpropagation) don't have any randomness to them; just to be sure, I checked this - which is a great explanation (not the simplest, but not impossible to follow):
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagat...
I don't know what your math skill level is here, so don't let any of the calculus and "chain rule" stuff get to you if you aren't familiar with it (tbh - I suck at it), just look at the equations and explanations. It's plain that there is no random number generator to be found in the process.
So - in theory - if the model, after it has been trained and "baked in place" so to speak - is presented with the exact same inputs, it should generate the exact same output.
But the input has to be exactly the same; in the case of an image, the neural network's input layer is usually an "unrolled" 1-dimensional array representing the pixel values of the image (left-to-right, top-to-bottom - as a 1D array - usually). Those values are usually grayscale or color values, presented either as integer data or floating-point values.
As long as that image data is presented exactly the same to the NN model, the output should be the same; for instance, if shown a set of pixels values that represent something, the output of the model will always be the same if shown those exact same pixel values.
But usually, these systems aren't built to take in "exact data" but rather data from sensors of the real world. So - the data that would probably be fed into the NN model likely comes from, say, a camera - and that sensor will not always present the exact same data to the NN model, even if purposefully set up to do so - because all such sensors have noise and aren't perfect (different pixels from the camera's sensor can and will return different values, even if shown a calibrated blank image in a fixed mode, with consistent fixed lighting, with the best camera sensor available, etc - it's just a fact of the real world).
So, because of this - the output of the NN model will in effect be "random" - but only because the input is effectively "random". That's actually ok, because what the model outputs (even if set up as a classifier) are values of probability - percentage values, where (ideally) the "spike" in the overall set of values represents the actual identification for the network, and that any inherent randomness in the system (sensors and whatnot) is filtered out and (hopefully) doesn't effect the outcome.
Though as we know, this too can be exploited; because the model is "baked in", you can show a series of images to the network, and get an identification on the other end, and probably with some statistical analysis you can work out what the layers in between might actually represent (probably not exactly though) - and identify flaws that could cause misidentifications on the output. More or less "hacking the NN model middle layers" and using that information to craft an "exploit" for the input to cause a particular mis-identification on the output side.
As we know - this is possible; at least, we know it's possible to get a network to output a wrong result by simply changing the pixel values of the input image slightly (even subtly that can't be seen by a person looking at the image - basically a steganographic style attack?). Such a "hack" could be used for any number of purposes, but mostly they show how fragile such NN models can be.
Does this mean they are random, or deterministic? I'd argue for the latter at this point, but again, I'm not really the person to ask. Hopefully someone else can answer (or has, by the time I post this).
That's actually an interesting question; I'm probably not the person to answer it, since my experience with NNs is admittedly on the "still learning" side of things, and that learning has not been via any credentialed sources...but I'm willing to take a stab at it.
NN models - that is, the thing that results from training a neural network, are typically "fixed", in that once trained, the model doesn't change - at least classically. I imagine that somewhere out there, there may be NN models which can change as data they are processing is run through them, learning on the fly so to speak. I'd have to research it; I'm sure it's something that's done or been done?
But normally, once a NN has been trained, it's model is "fixed" and doesn't change when inputs are presented to it. The training phase is - or appears to be - fairly stochastic. I'm not sure I'd want to call it random, though, because I don't recall any kind of random numbers being used during back-propagation.
But the data that is presented to the neural network is usually randomized in presentation - and sometimes content. That is, say you're training the NN on recognizing horses. You might have several thousand images of horses, but you don't want to show just those. You might want to generate many more - rotate each one just a bit, skew it just a smidge, maybe change the color and/or contrast/brightness, etc - to in effect generate a bunch more positive (and just as many negative - so you don't get bias or overfitting happening) examples to train the NN on what is a "horse" vs what is a "not horse".
So that data is somewhat "random" - but from what I recall, the actual mechanics of training - the algorithms of forward passes and backwards passes (backpropagation) don't have any randomness to them; just to be sure, I checked this - which is a great explanation (not the simplest, but not impossible to follow):
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagat...
I don't know what your math skill level is here, so don't let any of the calculus and "chain rule" stuff get to you if you aren't familiar with it (tbh - I suck at it), just look at the equations and explanations. It's plain that there is no random number generator to be found in the process.
So - in theory - if the model, after it has been trained and "baked in place" so to speak - is presented with the exact same inputs, it should generate the exact same output.
But the input has to be exactly the same; in the case of an image, the neural network's input layer is usually an "unrolled" 1-dimensional array representing the pixel values of the image (left-to-right, top-to-bottom - as a 1D array - usually). Those values are usually grayscale or color values, presented either as integer data or floating-point values.
As long as that image data is presented exactly the same to the NN model, the output should be the same; for instance, if shown a set of pixels values that represent something, the output of the model will always be the same if shown those exact same pixel values.
But usually, these systems aren't built to take in "exact data" but rather data from sensors of the real world. So - the data that would probably be fed into the NN model likely comes from, say, a camera - and that sensor will not always present the exact same data to the NN model, even if purposefully set up to do so - because all such sensors have noise and aren't perfect (different pixels from the camera's sensor can and will return different values, even if shown a calibrated blank image in a fixed mode, with consistent fixed lighting, with the best camera sensor available, etc - it's just a fact of the real world).
So, because of this - the output of the NN model will in effect be "random" - but only because the input is effectively "random". That's actually ok, because what the model outputs (even if set up as a classifier) are values of probability - percentage values, where (ideally) the "spike" in the overall set of values represents the actual identification for the network, and that any inherent randomness in the system (sensors and whatnot) is filtered out and (hopefully) doesn't effect the outcome.
Though as we know, this too can be exploited; because the model is "baked in", you can show a series of images to the network, and get an identification on the other end, and probably with some statistical analysis you can work out what the layers in between might actually represent (probably not exactly though) - and identify flaws that could cause misidentifications on the output. More or less "hacking the NN model middle layers" and using that information to craft an "exploit" for the input to cause a particular mis-identification on the output side.
As we know - this is possible; at least, we know it's possible to get a network to output a wrong result by simply changing the pixel values of the input image slightly (even subtly that can't be seen by a person looking at the image - basically a steganographic style attack?). Such a "hack" could be used for any number of purposes, but mostly they show how fragile such NN models can be.
Does this mean they are random, or deterministic? I'd argue for the latter at this point, but again, I'm not really the person to ask. Hopefully someone else can answer (or has, by the time I post this).
Thank you for taking the time for such a lengthy and in-depth reply. Greatly appreciated!
NNs have dramatically different architecture and abstractions from organic matter... advancements in neurological modelling and insights are better served by modelling based on dramatically different neural models than the ReLU and siblings. Neuroscientists do indeed investigate these biological models, but they are a far cry from NNs.
In the 40+ years since that study was done, it seems like psychiatrists still fail at distinguishing the sane from the insane!
Here's a fun story from Pennsylvania:
https://www.washingtonpost.com/news/morning-mix/wp/2018/01/0...
Here's a fun story from Pennsylvania:
https://www.washingtonpost.com/news/morning-mix/wp/2018/01/0...
"Sane" and "insane" are useless terms.
I wonder say that's a blow against the profession, but that is a blow against the individuals involved. The police are complicit in that whole thing as well, but we aren't trying to throw all of law enforcement out because of it.
I wonder say that's a blow against the profession, but that is a blow against the individuals involved. The police are complicit in that whole thing as well, but we aren't trying to throw all of law enforcement out because of it.
I'm almost positive that Freud did NOT think that "early-childhood sexual fantasies" were the basis of illness, but early-childhood trauma as the culprit, and I've come to believe that is almost surely the case, although I am sure no one cares about my insights on the issue.
Not only does this line up squarely with my own personal experience with addiction and other psychosis I have, but the lack of these issues in my children as well, who had a radically different early childhood then I and have no addiction issues whatsoever.
Case in point...I recently saw the documentary about the triplets that were separated at birth and given to 3 different socio-economic and emotionally-connected parents and who, by total accident, met at college and came to be national celebrities in the 70's and 80's.
One of the three eventually killed himself, due in part I'm sure to his much deeper addiction issues then that of his 2 siblings. The interesting part was that he was given to the "wealthy, but emotionally distant" family.
By far the most well balanced and successful of the 3 was the one given to the gregarious and emotionally-connected father, who showered the child with love and support throughout his whole life.
I could go on for 30 minutes discussing all the takeaways of this documentary, called Three Identical Strangers...it turns out the children where all part of an ethically-questionable experiment where, in my mind at least, the end results basically proves that 95% of the nature/nurture argument goes to the "nature" side.
It seems that all "nurture" can do is fuck you up and give you lifelong issues that are incredibly difficult to overcome.
Not only does this line up squarely with my own personal experience with addiction and other psychosis I have, but the lack of these issues in my children as well, who had a radically different early childhood then I and have no addiction issues whatsoever.
Case in point...I recently saw the documentary about the triplets that were separated at birth and given to 3 different socio-economic and emotionally-connected parents and who, by total accident, met at college and came to be national celebrities in the 70's and 80's.
One of the three eventually killed himself, due in part I'm sure to his much deeper addiction issues then that of his 2 siblings. The interesting part was that he was given to the "wealthy, but emotionally distant" family.
By far the most well balanced and successful of the 3 was the one given to the gregarious and emotionally-connected father, who showered the child with love and support throughout his whole life.
I could go on for 30 minutes discussing all the takeaways of this documentary, called Three Identical Strangers...it turns out the children where all part of an ethically-questionable experiment where, in my mind at least, the end results basically proves that 95% of the nature/nurture argument goes to the "nature" side.
It seems that all "nurture" can do is fuck you up and give you lifelong issues that are incredibly difficult to overcome.
Sigmund Freud was somewhere between a fraud (accessory to rape, in that he systematically defended rapists), to a serial rapist himself. That any of his theories are used at all in any psychological course is shameful.
https://www.nytimes.com/1984/01/24/science/freud-secret-docu...
(search for "real rape" in that article to read one account of Freud expending a lot of effort to hide obvious cases of rape)
https://www.nytimes.com/1984/01/24/science/freud-secret-docu...
(search for "real rape" in that article to read one account of Freud expending a lot of effort to hide obvious cases of rape)
The problem is truly helping the severely damaged is such a difficult task requiring long term, intense intervention that most mental health professionals just fall back onto the standard approach of pathologicalizing behavior and prescribing medication. Theirs is just a job too at the end of the day, is it reasonable to expect them to carry the weight of society's failures?
> psychiatrists should devote themselves to solving the problem of serious psychoses, and should concede responsibility for what she describes as mental suffering that is not a true illness to therapists and social workers.
> > I disagree.
Saved you the time of reading this fancy opinion piece.
> > I disagree.
Saved you the time of reading this fancy opinion piece.