Part of the reason why I’m able to learn what I’m learning is that my partner, Justin, is an academic. He’s built his career on reading, writing, and analyzing journal articles, which means he’s my first stop on the understanding research train. This is both great and terrible for me. On the one hand, I have an expert at my disposal. On the other hand, I have an expert at my disposal. What I think are straightforward questions turn into twenty-minute tirades that leave me more confused than before. No answer is ever simple, and I’ve been forced to accept that “it depends” is a valid conclusion.

“The more you research you read the more you’ll understand that every single study is fundamentally flawed,” he said to me yesterday. “Be careful about assumptions, because research studies are full of caveats and exceptions. They’re looking at one little sliver of one thing, and there’s no easy way to accurately translate that into something digestible and catchy for the media.”

All this because I asked him what n meant in a paper.

I assumed the n operated like it does algebra, standing for a constant throughout the entire paper. As it turns out, that is entirely incorrect. There are big Ns and little ns. The big N typically stands for population size while the little n stands for some sort of value. For example, if there are 1000 people in a school but only 200 of them were chosen for a study, N=1000 and n=200.

However, the n does not necessarily refer to human subjects and the meaning of that n can change with context. Using the paper from yesterday’s post as my example, we can see that there are a variety of values for n throughout different parts of the article. The first shows up in the abstract, n=16:

Reading the sentence before it, “antidepressants were significantly better than placebo in trials that had a low risk of bias,” this little n refers to the number of studies analyzed that had a low risk of bias (16 studies.) Why they can’t just say, “In the 16 trials that had a low risk of bias…” I don’t know.

Further down the paper, n shows up again:

To understand what these ns represent, we need to read for context. The previous page states, “The literature searches from databases and additional resources identified 2890 relevant titles.” In this case, n has to do with the number of studies analyzed, and the chart breaks down how the researchers began with 2890 studies (2864 records identified through database searching + 26 records identified through other sources) and whittled their relevant studies down to the 28 included in the meta-analysis.

To sum up: An n is not an interpretation of the data but instead communicates some sort of numerical value. That value changes depending on what it’s referring to, so it’s always necessary to read for context.

When I first began speaking openly about long term antidepressant use and antidepressant withdrawal, it didn’t take long for me to be faced with a wall of academic journals and research papers. At first, my instinct was to read the abstract, get the gist of what I was trying to understand and move on. But much like sourcing all your information exclusively from Fox News, that approach left me a dangerous kind of dumb. I had just enough information to confirm my bias but zero original thoughts surrounding the source, scope of work, journal reputation, limitations of the study, and industry response.

When it dawned on me that just reading the abstract was no better than just reading sensational news headlines and deeming yourself informed, I began to read the studies in full. At least, I tried. For those of us who haven’t spent their entire adult lives in research and academia, these papers are a nightmare.

While I understand that there are longstanding reasons why academic papers are written the way they’re written, it bothers me that only people with a PhD are taught to comprehend this sort of work. How can the individual be expected do their own research and make their own decisions for their own wellness if they can’t understand the research that policy and marketing is built upon?

Which brings me to the first installment of How to Read a Scientific Paper. I’m tired of taking other people’s word on research as gospel, so I’m going to learn how to do it myself and chronicle the journey here. Hopefully, I can beef up the entertainment factor, because damn these articles are dry.

I’m going to begin with a recent article spearheaded by psychiatrist Saeed Farooq and published in the Journal of Affective Disorders, entitled, “Pharmacological interventions for prevention of depression in high risk conditions: Systematic review and meta-analysis.”

I first found out about the study thanks to a Keele University tweet that said, “The study, led by Professor Saeed Farooq, found that using antidepressants as a pre-emptive measure could help to prevent depression in patients considered to be at high risk of developing the condition, for example following stroke or heart attack.” The tweet linked not to the article, but an in-house blog post that feels a bit too much like propaganda. The fact that we’re even considering doping people up on antidepressants before they become depressed deeply concerns me, so I want to learn more about it before I go full oh no you di’n’t! on the topic.

In reality, this was not a research study or clinical trial, but a systematic review and meta-analysis. And for us to learn to read journal articles, we must understand the difference.

A research article is a study designed and performed by the paper’s author or authors. It will explain the methodology of the study—or rather, the methods and systems used to conduct the study—and clarify what the results mean. All of the steps are listed in detail in order to allow other researchers to conduct similar experiments.

One of the best ways to tell if you’re reading a research article is to look for phrases like “we found” or “I measured” or “we tested.” This indicated that the authors who are writing the article are the ones who also conducted the research.

Next, look at the formatting of the article. Research papers include sections that are listed in a particular order: abstract, introduction, methods, results, discussion, and references.

Review papers do not include original research conducted by the authors(s). Instead the author(s) give their thoughts on existing research papers for the purpose of identifying patterns or forming potential new conclusions based on a variety of research studies. For example, a researcher may look at a study performed in 1980 and compare it to a similar study from 2010 in order to provide an overview of the topic as a whole.

Reviews are particularly useful for people looking to get background information on a topic before diving into detailed or technical research papers. However, there is no formal process to dictate which articles must be included in a review, which gives authors the freedom to overlook existing research that may not fit their agenda. Thus, it can be difficult to determine if the author’s conclusions are biased.

Systematic reviews were developed to eliminate that bias by requiring multiple authors to track down all available studies on a particular topic and execute high-level analysis of existing research in order to answer a clearly defined, clinical question. Systematic reviews can take months or years to complete, whereas standard reviews may only take a few weeks.

Systematic reviews contain a lot of data and to the untrained eye, can look a lot like original research. Systematic reviews are held in the same echelon as original research and are often presented to the public as if the research was new (like in the Keele University tweet.) This strikes me as potentially misleading, not because the research isn’t valid or useful, but because of the language used to promote the research.

For example, Farooq’s article concludes that based on his analysis, “Prevention of depression may be possible in patients who have high-risk conditions but the strategy requires complete risk and benefits analysis before it can be considered for clinical practice. However, not a single clinical study has been conducted to support or disprove that statement and the tweet says nothing about that and instead presents the research as if it were a new, exciting discovery.

Meta-analysis is a research process used to manage and interpret all the data for a systematic review. In layman’s terms, meta-analysis is how researchers make sense of the data in hundreds or thousands of individual papers. After extracting the data, analysts use a variety of methods to account for differences like sample size, variations in study approach that may affect the overall outcome of the systematic review, and overall findings.

Frankly, I don’t understand a lick of how meta-analysis works. But, I’ve learned that I don’t have to understand it as long as I understand what role it plays in research: meta-analysis pools the data sets from different studies into a single statistical set of data in order to analyze it and come to a single conclusion.

*  *  *

For or those of you who like visuals, check out this article by Concordia University that visually breaks down the structure of various journal articles so you can recognize what you’re reading.

I’ve said it before and I’ll say it again: I am not a doctor. I am also not a researcher, pharmacist, or psychologist. I don’t have a PhD. Or a Masters. My Bachelor’s degree is in history. Not a specific part of history, but of all time. I’ve also got a culinary degree that ultimately led me to compete on—and win—Food Network’s “Chopped,” as well as an XPT Life certification that allows me to coach movement and breathwork.

All this to say: On paper, I’m no psychiatric expert.

But life has a funny way of shoving us down unexpected paths, and despite a resume that suggests my time is best spent in the kitchen or the gym, I now find myself as an emerging voice in the fight against the depression and antidepressant epidemic.

I would be lying if I told you that I was happy to hold this torch. But like an avalanche that can’t be stopped, I sealed my fate when I tipped a snowball over the mountain back in July of 2017 and agreed to write a memoir about my year of international travel. The book was to be called Ladyballs, and it would have a snarky, boss bitch attitude about leaving a shitty life for one full of global adventure. Eat, Pray, Love for disillusioned millennials.

Disgusting, right?

Like most work that overleans on sarcasm, the book’s irreverent attitude was a coverup for the story I was still too ashamed to tell: I’d spent half my life on antidepressants, and after a hell year of getting off them, I had no idea who I was or what I was supposed to do with myself.

Ladyballs ultimately fell through, leaving me with nothing but a shitty first draft of a book no one should ever read. But thank God for that shitty draft, because buried in it was nuggets of the real story, the story of what happened after I booked a one-way ticket to Malaysia and got off fifteen years of antidepressants, one by one by one by one by one. As of today, my memoir May Cause Side Effects is out for submission.

Which brings me here. I spent the last two and a half years writing May Cause Side Effects, with no guarantees that it will ever get published. While my agent is busy doing her job, I am tasked with pivoting away from my image as a chef and to what they call, a “recognized expert” in the field. And since I don’t have letters after my name that automatically deem me an expert, I’ve got a different sort of work to do.

For years, I’ve been thinking about how I can use my experience to add value to the conversation surrounding antidepressants without making black or white statements, alienating other people’s choices, or getting overly political. Now that I’ve been published in a major news outlet, started seriously tweeting, and given a few speeches on the topic, I’ve come to the solemn understanding that there’s no undivisive way to enter into the conversation about antidepressants. Like climate change and income inequality, depression and antidepressants are inherently political. The message consumers are presented with is born in a profit-driven marketing machine fueled by researchers who depend upon government money to conduct narrow studies that result in limited data extracted by pharmaceutical companies who funnel billions of dollars into government policy and television commercials in order to convince you that your problems are all in your head.

Did your eyes glaze over a little bit during that sentence? Don’t worry, it’s not your fault. You and millions of other people have a mental illness, just like millions of people have diabetes! The brain is an organ, just like the pancreas. Diabetics take insulin for a faulty pancreas, so why not take antidepressants for a faulty brain?

Except despite a few decades of rampant and rising antidepressant use, depression and suicide rates continue to rise, so much so that psychiatrists from Keele University just published a review hypothesizing that prescribing antidepressants before someone becomes depressed might lower their chance of developing depression.

That’s like giving healthy people chemo just in case they get cancer.

Which brings me to why I’m here. My work over the past few years has led me to believe that without a (highly unlikely) overhaul of our entire mental health and healthcare system, the onus is on the individual patient to do the research and take their treatment, therapy, and healing into their own hands-or face the consequences of unknown, unsubstantiated long term antidepressant drug use. This means that people need to think for themselves, learn how to do their own research, and unscrew the notion that we have any real understanding of what causes depression. Because we don’t. And I don’t see us cracking that code anytime soon.

That said, I want to emphasize the following: Since getting off all my antidepressants, I have been honored to work with a variety of outstanding medical professionals, from psychologists to researchers to psychiatrists. There are solid humans out there working to help people truly get better. This is a stark contrast to the psychiatric and psychological experiences I had as a young adult, and I regularly wonder whether or not my life would have taken the same course if I hadn’t had shit psychiatric luck so early in my life.

But I did, so here we are.

My goal is to take readers through my own process of learning, uncovering, and understanding this complex issue. I reserve the right to question what I’ve been told, to change my mind, and to make mistakes. I can’t promise that I’ll always be right. But I can promise to admit when I’m wrong. Because the only truth I know is the one I experienced, and that’s not enough for me.
If you’ve made it this far and you like what I’m doing, I’d appreciate it if you could give me a follow on Twitter or share my work with someone who might appreciate it.

Thanks for sticking with me,
Brooke