In August, I have decided to resign from my volunteer post at the marijuana research lab. I have been thinking about my future course of action for a few months now, and after much thought and discussion, I realize that it's time for me to move on from this lab.
There were several reasons why I resigned. As I discussed a few times throughout these blog posts, the pace of the laboratory clearly sets a series of limits regarding what can be done in the lab. Since our work can only go as fast as the experimental cycle is run, there were often times where there was not enough work to go around to all of the workers in the lab. I felt a bit frustrated about the slow pace, and I often wished that there was more I could do. Furthermore, as a volunteer, there was an obvious limit to the amount of responsibilities that I could have at the lab. An RA has a range of tasks and responsibilities around the lab, which all revolve around the execution and analysis of the research. Our RAs were given much more responsibility than the volunteers due to their educational and work experience, and the volunteers could only assist in a variety of different tasks (data entry, taking vital signs, etc). Although there were many tasks to keep me occupied, after working at the lab for 12 months, I had felt like I had already done most of what I could do there many times.
Additionally, it will be great to take a few months to relax and focus on my classes and hobbies during a semester. I am usually so packed with many things to do - between work, clubs, classes, and homework, I felt like I did not have sufficient time to put my full effort into any one of these activities. Now that I am taking a semester break from working on research, I hope that I can experiment with spending more time on my classes and my extracurriculars.
Nonetheless, this research and this blog has been fun! I've learned a lot! I appreciate whoever is reading this for taking the time to do so!
Friday, September 5, 2014
DEA Visit
Today, the lab had a DEA inspection [Note: this note refers to a day in June]. Although I do not know too much about the DEA procedures, I assume that this was a routine visit in order to ensure that the lab is up to standards in terms of safety and security. Since marijuana is a Schedule I drug in the United States (which means that the drug has a high potential for abuse, the drug has no currently accepted medical use in treatment in the United States, there is a lack of accepted safety for use of the drug under medical supervision), the DEA are making a sort of "exception" to the law in order for the lab to use marijuana for research. Thus, they must make sure that we are doing everything in our power to make sure that the drug is being used in a safe and controlled manner.
As mentioned in a much earlier blog post, one of the most important parts of the facility is the "drug room," where we store all of the substances that are being studied in the SURC. This room has many highly illegal drugs, and thus must be protected with the strict security standards. The part of the inspection that I witnessed mostly had to do with checking the various systems that we have in place to protect this room (there are many!). Alarms were tested, codes were used, and there were even some parts where I was told to cover my ears so I wouldn't hear a particular part of the security protocol.
So, as I've mentioned several times throughout this blog, security for the research of schedule I drugs is extremely important, and the protocols that we follow in the lab ensure that we can maintain the trust of the government. Although it may be nerve wracking for the staff to have DEA officials come into our lab and scrutinize our process, this is the necessary price to pay for doing important research that will affect many people.
So, as I've mentioned several times throughout this blog, security for the research of schedule I drugs is extremely important, and the protocols that we follow in the lab ensure that we can maintain the trust of the government. Although it may be nerve wracking for the staff to have DEA officials come into our lab and scrutinize our process, this is the necessary price to pay for doing important research that will affect many people.
Who Works Here?
I'll take the time to write this post about what sort of people work alongside me in the research lab. Since the NYSPI is a huge institution, of course there is a large hierarchy of people who work above the researchers - administrators, board members, etc. However, in the realm of the actual research, the PI (primary investigator) is in charge of the research. He or she deices what the content of the research project will be - this includes the subject being investigated, the method in which the research will be conducted, and the interpretation of the results. These PI's usually have doctorates in clinical psychology, neuroscience, biology, or other related fields, and have to go through very long application processes, especially when it comes to applying for grants that allow them to do this research.
I'll spend more time describing the background and work of research assistants, whom I've spent much more time working directly with. RA's usually have a BA in a relevant subject, and since an RA position at the lab is considered a "transition" job, RA's typically plan to go to medical or graduate school in the future. The RA contract states that he or she must work there for about a year, during which they are working ~40 hour work-weeks (it's a full time job!), which include some weekends during experimental cycles. The RAs that I have encountered have all planned to be there for about 1.5-2 years, after which they plan to go on to graduate or medical schools (the two that I worked with directly in the Res Lab want to go to medical school, while the RA in the outpatient lab was looking towards graduate school).
So becoming an RA in the lab seems to be a useful step in the process of getting into grad/medical school. Many people suggest taking a break after undergrad before going on to the next level of education. This allows time to get valuable work experience that can be added to one's resume and application. Schools seem to look favorably upon those who have shown the maturity and responsibility required to run a lab. Although I am still a couple of years away from making this sort of decision, it is definitely something I will now consider!
I'll spend more time describing the background and work of research assistants, whom I've spent much more time working directly with. RA's usually have a BA in a relevant subject, and since an RA position at the lab is considered a "transition" job, RA's typically plan to go to medical or graduate school in the future. The RA contract states that he or she must work there for about a year, during which they are working ~40 hour work-weeks (it's a full time job!), which include some weekends during experimental cycles. The RAs that I have encountered have all planned to be there for about 1.5-2 years, after which they plan to go on to graduate or medical schools (the two that I worked with directly in the Res Lab want to go to medical school, while the RA in the outpatient lab was looking towards graduate school).
So becoming an RA in the lab seems to be a useful step in the process of getting into grad/medical school. Many people suggest taking a break after undergrad before going on to the next level of education. This allows time to get valuable work experience that can be added to one's resume and application. Schools seem to look favorably upon those who have shown the maturity and responsibility required to run a lab. Although I am still a couple of years away from making this sort of decision, it is definitely something I will now consider!
Thursday, September 4, 2014
On Advertisements and Demographics
One interesting thing about the lab is how we recruit individuals to participate in our study. Currently, our lab employs two methods of advertising for participants: through newspapers and the internet. We post ads on the Village Voice, AM New York, and Craigslist. These are all free publications and websites, which ensures that we are not excluding any demographic that wouldn't pay for these services (we would be targeting a different group of people if we were advertising in the New York Times). Additionally, there are a number of people who claim to have heard of the study through a friend or family.
Most people who call in say that they saw the advertisement in AM New York, a few say that they saw it in the Village Voice, and almost none say they saw it on Craigslist. There are a few explanations on why this happens, all of which probably reflects and interacts with the demographics that we deal with in the study. Our study is mostly populated by lower class black and hispanic males between the ages of 20-35. Thus, newspapers that are distributed for free, especially if they are distributed in high-traffic areas of public transportation, would more directly target the demographic that is likely to inquire about our study.
Craigslist has always struck me as an interesting case, since very few of our participants say that they heard about our study through the website. I have a few hypotheses on why this may be true. Could it be that men of minority backgrounds use the internet less? PewResearch (www.pewinternet.org) data says that this is true, but only slightly. The same website shows the data that economic factors play a larger role, stating that those who make over $75,000 a year are almost a 1/4 more likely to use the internet than those who make less than $30,000 a year. Other factors might include the general perception of newspapers as a tool to find jobs, rather than Craigslist, which is much newer and may only be thought of as a marketplace to those who are less familiar with the website.
However, even though we realize that Craigslist is less effective at drawing in participants, it is a free service, so we will continue to use it in order to spread the word about the study.
Most people who call in say that they saw the advertisement in AM New York, a few say that they saw it in the Village Voice, and almost none say they saw it on Craigslist. There are a few explanations on why this happens, all of which probably reflects and interacts with the demographics that we deal with in the study. Our study is mostly populated by lower class black and hispanic males between the ages of 20-35. Thus, newspapers that are distributed for free, especially if they are distributed in high-traffic areas of public transportation, would more directly target the demographic that is likely to inquire about our study.
Craigslist has always struck me as an interesting case, since very few of our participants say that they heard about our study through the website. I have a few hypotheses on why this may be true. Could it be that men of minority backgrounds use the internet less? PewResearch (www.pewinternet.org) data says that this is true, but only slightly. The same website shows the data that economic factors play a larger role, stating that those who make over $75,000 a year are almost a 1/4 more likely to use the internet than those who make less than $30,000 a year. Other factors might include the general perception of newspapers as a tool to find jobs, rather than Craigslist, which is much newer and may only be thought of as a marketplace to those who are less familiar with the website.
However, even though we realize that Craigslist is less effective at drawing in participants, it is a free service, so we will continue to use it in order to spread the word about the study.
Wednesday, September 3, 2014
Automation - An Idea
One idea that I have considered over the last few months is the possibility of automating some of the aspects of our database. All of our data is handled in Excel spreadsheets. Since we handle a huge amount of data that contains demographic information, medical records, test results, waivers, and more, the lab's two computers contain a large body of files and folders that hold all of the data that pertain to the experiment. As you can probably assume, maneuvering through all of these spreadsheets can get very confusing. There are many cases in which one spreadsheet utilizes information collected from another spreadsheet (for example, to complete our "Participant Master" spreadsheet, we need to collect information from spreadsheets about the background of the participant, the medical history of the participant, our past interactions with the participant, etc). Thus, the many spreadsheets are not only confusing, but become very time consuming as they demand that the data be entered multiple times into several spreadsheets.
My idea involves streamlining this process by having some sort of computer program that links the relevant spreadsheets, so that the data from the original spreadsheets be automatically entered into other spreadsheets that require that information. For example, the "Participant Master" spreadsheet would be linked to all of the other spreadsheets that contain the necessary information to complete that spreadsheet. Once we fill in all of the information for those sheets, it would automatically be linked and entered into the "Participant Master" spreadsheet.
This seems like a simple enough idea, and I have brought it up to some of my coworkers before. They aren't sure why the primary researchers don't pursue this idea - it may be because nobody has the necessary skills to do it (including myself!), and that it would not be worth the cost or effort to find someone who has the skills to do it. Additionally, the more technology we use, and the more complicated our programs become, the more likely we will have some sort technological failure.
My idea involves streamlining this process by having some sort of computer program that links the relevant spreadsheets, so that the data from the original spreadsheets be automatically entered into other spreadsheets that require that information. For example, the "Participant Master" spreadsheet would be linked to all of the other spreadsheets that contain the necessary information to complete that spreadsheet. Once we fill in all of the information for those sheets, it would automatically be linked and entered into the "Participant Master" spreadsheet.
This seems like a simple enough idea, and I have brought it up to some of my coworkers before. They aren't sure why the primary researchers don't pursue this idea - it may be because nobody has the necessary skills to do it (including myself!), and that it would not be worth the cost or effort to find someone who has the skills to do it. Additionally, the more technology we use, and the more complicated our programs become, the more likely we will have some sort technological failure.
On Error
Today I want to write a bit about human error and it's role in our research. Although science attempts to interpret the world in an objective manner, errors in both the recording and subsequent interpretation of data can call our scientific method into question.
We use a variety of different types of data in this research. Some of this data is self-reported by the participant. I had already written a post about the problems that stem from self-reported data, since participants can often lie about themselves (how much they smoke, their medical history, etc). Although we are able to figure out some of these lies through a series of interviews and medical tests, they often hurt the process of our research by wasting time and resources on participants who are not actually eligible.
However, another sort of error comes from us, the researchers. I will talk about how these errors occur, and what we do to prevent them.
The first consideration to make about data lies in the instruments that we use to record our measurements. There are a few instruments - rulers, weight scales, blood pressure machines, CO breathalyzers, etc. We use these machines to help make accurate measurements that could not be made by humans themselves. However, there can be mistakes made through these instruments (whether it is caused by a faulty mechanism or by an error in the machine's usage). This means that the data we use is wrong, and would thus skew the results and conclusions we draw from the data. Errors in measurement can be counteracted by doing frequent measurements. Although this is done to measure the change in values over time, by comparing any value to the values next to it, we are able to make a judgement about how accurate our measurements are.
If we do indeed collect the data correctly, an error could be made in the database maintenance aspect of the research. For example, while entering our data into Excel spreadsheets, mistakes can easily be made by entering the wrong information into the spreadsheet. The best way that we can combat this is to triple check the data once it is entered. Thankfully, since we have a large number of bodies in the room, there are always multiple people who are willing to look at the same data to make sure that it is accurate.
We use a variety of different types of data in this research. Some of this data is self-reported by the participant. I had already written a post about the problems that stem from self-reported data, since participants can often lie about themselves (how much they smoke, their medical history, etc). Although we are able to figure out some of these lies through a series of interviews and medical tests, they often hurt the process of our research by wasting time and resources on participants who are not actually eligible.
However, another sort of error comes from us, the researchers. I will talk about how these errors occur, and what we do to prevent them.
The first consideration to make about data lies in the instruments that we use to record our measurements. There are a few instruments - rulers, weight scales, blood pressure machines, CO breathalyzers, etc. We use these machines to help make accurate measurements that could not be made by humans themselves. However, there can be mistakes made through these instruments (whether it is caused by a faulty mechanism or by an error in the machine's usage). This means that the data we use is wrong, and would thus skew the results and conclusions we draw from the data. Errors in measurement can be counteracted by doing frequent measurements. Although this is done to measure the change in values over time, by comparing any value to the values next to it, we are able to make a judgement about how accurate our measurements are.
If we do indeed collect the data correctly, an error could be made in the database maintenance aspect of the research. For example, while entering our data into Excel spreadsheets, mistakes can easily be made by entering the wrong information into the spreadsheet. The best way that we can combat this is to triple check the data once it is entered. Thankfully, since we have a large number of bodies in the room, there are always multiple people who are willing to look at the same data to make sure that it is accurate.
Friday, August 22, 2014
The Smoker
This participant was a very heavy smoker. I cannot remember the specific amount he claimed to smoke throughout the day, but I assume it was at least more than one pack. One way we could measure the level of smoking of our participants would be measuring CO levels. The participant blows into an apparatus (similar to a breathalyzer), which would then report the amount of residue from smoking cigarettes that remains in the participant's lungs. There is always a large range of CO measurements, depending on how heavy of a smoker someone is, as well as the last time the participant had a cigarette. Since we usually measure the CO levels when the participants first walk in to the lab, the levels are often sky-high since people often have cigarettes right before they walk into the building.
The particular participant had CO levels that were much higher than normal, even for someone who had smoked recently. This let us know that he was a very heavy smoker, which is ideal for our lab that is studying cigarette cessation in relation to marijuana use.
However, after having to deal with this participant, we may have experienced a smoker that was simply too addicted to participate in our study. During our screening process, participants come in between 9 AM and 5 PM in order to be trained and further examined for study eligibility. The day is long and boring, but it acts a perfect simulation for the conditions of the Inpatient segment of the study. Although participants do get several smoke breaks throughout the day, this particular participant could not wait for his breaks. He begged the staff to let him have frequent breaks, claiming that he could not function for long without smoking. We did let him have more frequent smoke breaks; however, this let us know that the participant would not be able to withstand the long, smokeless hours that would make both the Inpatient and Outpatient days. Furthermore, although most participants have a variety of negative reactions to quitting cigarettes, he would have reacting extremely negatively to the "quit day" (when the participants would have to stop smoking cigarettes). This participant showed that there are indeed too extreme people for our study.
The particular participant had CO levels that were much higher than normal, even for someone who had smoked recently. This let us know that he was a very heavy smoker, which is ideal for our lab that is studying cigarette cessation in relation to marijuana use.
However, after having to deal with this participant, we may have experienced a smoker that was simply too addicted to participate in our study. During our screening process, participants come in between 9 AM and 5 PM in order to be trained and further examined for study eligibility. The day is long and boring, but it acts a perfect simulation for the conditions of the Inpatient segment of the study. Although participants do get several smoke breaks throughout the day, this particular participant could not wait for his breaks. He begged the staff to let him have frequent breaks, claiming that he could not function for long without smoking. We did let him have more frequent smoke breaks; however, this let us know that the participant would not be able to withstand the long, smokeless hours that would make both the Inpatient and Outpatient days. Furthermore, although most participants have a variety of negative reactions to quitting cigarettes, he would have reacting extremely negatively to the "quit day" (when the participants would have to stop smoking cigarettes). This participant showed that there are indeed too extreme people for our study.
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