Tutorials

An excerpt from a pre-publication version of:

Belskie, M., Zhang, H., & Hemminger, B. M. (2023). Measuring Toxicity Toward Women in Game-Based Communities. Journal of Electronic Gaming and Esports, 1(1).

Literature Review

What is toxicity and taxonomy of toxicity

Although there is some disagreement on what might constitute toxicity, we framed part of our own usage to be consistent with Beres et al. (2021) to mean “various types of negative behaviors involving abusive communications directed towards other players and disruptive gameplay that violates the rules and social norms of the game.” Other definitions have included “negative behaviors exhibited by players in online environments” (Türkay et al., 2020) or a hodgepodge of terms including “incivility, griefing, and degrading comments” (Shen et al., 2020). Although the Beres et al. (2021) definition lacks some specificity it is still a comprehensive definition because it includes the actors – both those who are toxic and those who are recipients of toxicity, it makes clear that toxicity is comprised of actions and comments, it acknowledges the role of cultural norms, and it is framed within a context of esports.

The taxonomy presented by Beres et al. (2021) was partially useful in describing the toxicity that we saw in posts. Their framework includes flaming, trolling, griefing, and spamming as types of toxic language or behavior evident in games. Another taxonomy presented by Mall et al., (2020) focused instead on patterns of toxic behaviors by users across time and was comprised of fickle-minded, steady, radicalized, and pacified users which was less useful for our research objectives. Xia et al. (2020) are less concerned with a strict definition of toxicity and instead focus on a two-part dynamic of things that are toxic or things that cause toxicity. The Perspective API (Perspective Developers: Attributes & Languages, 2022) taxonomy includes items for Toxicity, Severe_Toxicity, Identity_Attack, Insult, Profanity, and Threat. The taxonomy of Risch and Krestel (2020), containing classifications for profanity, insults, threats, identity hate, and “otherwise toxic” was deemed to be most useful to our research for a few reasons. One, that they helped us to address whether comments toxic towards women more frequently of a specific type, two, they permitted us to analyze how accurate automated tools are at identifying different types of toxicity, and finally, it mapped neatly onto the Perspective API analysis that we used.

Toxicity toward Women in games and game communities

Toxic language and behavior, particularly towards women, are popular in games and game communities. Even when content is scrubbed, a lot of the damage has already been done as was seen in the #GamerGate and Fappening controversies on Reddit. In these cases, the management (or lack thereof) of content in forums related to these topics actively contributes to increased toxicity – especially toxicity towards women (Farrell et al., 2019; Massanari, 2017). In a chapter by Andrews and Crawford (2021) looking at successful examples of mixed gender sports – equestrian sports and riflery – they noted that safe and non-toxic environments were minimum requirements for successful participation by women.

Darvin et al.’s (2021) contributions have been significant with a strong qualitative study of ten women representing professional gamers and executives in the esports industry. Their key findings include that much of the evident toxicity is both top down and bottom up. The companies that make the games which create these communities are themselves subject to toxicity allegations. These same patterns of toxicity are evident at every level of the esports vertical, from upper management down to casual communities. They also uncovered that Perceptions that women just “aren’t interested” in esports are false and look like what they did for women in traditional sports prior to creation and implementation of Title IX. A similar study by Hayday and Collison (2019) conducted focus groups for 65 participants followed up by interviews of 16 participants and likewise discovered that the effects of toxic masculinity in the space greatly contributed to the reported negative experiences of women in esports. Both studies together help to illustrate the ways that toxicity in esports and esports communities is actively perceived by women as being harmful, and not just theoretically harmful.

Much of the toxicity towards women in games, and one borne out by comments in game communities, takes the form of expectations for women to only fulfill certain roles in games (Ruotsalainen & Friman, 2018). Where this is damning for women is that they are subjected to verbal abuse and other toxicity for selecting these “pre-approved” roles in games – specifically Support roles or female characters – when they do and are also subjected to verbal abuse and other toxicity when they depart from these “permitted” roles for not “knowing their place”. These attitudes are further compounded by the fact that when women do experience in-game success with these characters they are branded as “one-tricks”, a derogatory insult meant to imply the player is unskilled, and that their success is ill-gotten. This precarious condition for inclusion in esports and esports communities makes these spaces especially toxic to women and functions to actively exclude women.

While literature exploring toxicity towards women in games is abundant, the existing literature is incomplete. There is a lack of taxonomic understanding of toxicity directed toward women, which contributes to the lack of understanding for why current approaches toward automated toxicity detection are insufficient to quantifying toxicity towards women, and thus inadequate to ameliorating the issue.

Measuring and detecting toxicity

Methods for measuring or detecting toxicity can be categorized as manual or automatic processes. Manual processes include in-game reporting within games or human assessment of toxicity in games or game communities. Automated processes typically involve either semantic analysis with natural language processing or applying corpora of terms to game or forum logs to measure counts of specific words or phrases (Brassard-Gourdeau & Khoury, 2018; Jurgens, Chandrasekharan, & Hemphill, 2019; Noever, 2018). These automated processes, such as the Perspective API, produce a likelihood of toxicity so that human intervention then decides on what threshold to positively identify toxicity.

There are numerous examples of studies using Perspective or other tools for automatically assessing toxicity in forums. A common goal some of these studies share is to create better tools to aid in detection of toxic comments for the purpose helping to better automoderate communities. While some of the selected literature does not explicitly focus on detecting toxicity in games or game communities, their methods and conclusions prove useful to our own understanding and underline the gaps that still exist in this space.

In one such paper that looks at toxicity detection in a general context, Noever (2018) points to a few extant flaws in the Perspective API. Of these, the biggest seems to be a lack of transparency for how it works. His work explores alternative strategies for automatically detecting toxicity, and two promising conclusions he points to are the value of ensemble strategies – using many weaker, cheaper, or faster platforms to form an aggregate judgment, and using tree-based algorithms to apply faceted analyses to content.

One idea presented is detecting toxicity before it occurs based on maximum likelihood. Where most measurements are a posteriori, a few papers look to better explore and explain the antecedents or triggers to toxicity and toxic comments (Almerekhi et al., 2020;  Almerekhi, Jansen, & Kwak, 2020; Jurgens, Chandrasekharan, & Hemphill, 2019; Shen et al. 2020; Xia et al., 2020). These triggers can frequently be benign and non-toxic themselves, but still act as tent-poles for toxic comments and reactions (Almerekhi et al., 2020). A common shortcoming exhibited in these studies is their reliance solely on automated detection of toxicity in their analyses. Where the automated tools are inaccurate in their toxicity detection, and we posit that there are places where they are, conclusions can be misleading or inaccurate.

Human measurement of toxicity is critical to improving the datasets that inform machine learning approaches. A particularly good example of a method is found in a paper by Carton, Mei, and Resnick (2020) that uses consensus coding for comments. Where this approach is lacking, and the authors acknowledge this limitation, is that no information is provided indicating how competent the human coders are at detecting toxicity. Optimal coding would utilize either an ensemble process with many human coders, or a smaller set of expert coders. The normalization of toxic language and behaviors is covered in other research (Beres et al., 2021), and non-expert coders are a limitation to the accuracy of human consensus coding. And seeking to add explanatory functions to automated detection of toxicity for aiding human detection can in fact confound human scoring of that toxicity (Carton, Mei, & Resnick, 2020).

Detection of toxicity can diverge across different detection systems. Although the research by Venkit and Wilson (2021) focused on people with disabilities, a secondary discovery they made in their research was that different systems (DistilBERT, Perspective (Google API), TextBlob, and VADER) produced different levels of sentiment score for identical statements. Their paper provides some support for the ideas of others to build consensus systems of automated detection of toxicity, but also highlights limitations shared across all those systems to prioritize and overestimate the toxicity of certain terms without sensitivity to context.

Sentiment analysis is one technique used in attempts to improve the results of automated detection of toxicity. The rational for this approach is that toxic comments can be coded (e.g., using “leet speak” or other methods) in ways to make it harder for automated tools to compare that word against a corpus of known toxic terms, but that sentiment will be more difficult to disguise. Brassard-Gourdeau and Khoury (2020) found that sentiment detection could refine and improve classification of comments as toxic, but it should be noted that those results were compared to a dataset that itself was scored for toxicity by an automated system rather than expert human coding which leaves their conclusions open to the same limitations as the automated scoring process.

The primary gap we see in the scoring and toxicity measures described in the literature to date is a focus on agreement as a percentage in favor of accuracy. Jurgens, Chandrasekharan, & Hemphill (2019) call out to this state of research that seems more focused on simple measurements without critical analysis of what those measurements mean or how they should be used. If a human and Perspective are coding a set of one hundred comments both could agree that there are forty toxic comments, and in this case, both agree that 40% of the comments are toxic. The problem is those could be non-overlapping sets, and the human and AI do not agree on which forty comments are toxic. That automated tools are in fact frequently incorrect when detecting certain types of toxicity is not unknown (Almerekhi et al., 2020), but it is also not explored at sufficient depth. Methodologically it appears that a common problem is that the human coding utilized by other projects is calibrated using the Perspective API literature, and we believe this contributes to artificially inflated agreement. Our research goes a step further to describe which toxic taxonomies have higher and lower rates of agreement and relies on expert human coding.

Normalization of toxicity

Culture begets culture. The effect of bad actors working in concert can have outsized effects because of recommender systems in Reddit, which contributes to establishment of toxic norms in Reddit communities (Massanari, 2017). These norms in turn can alter the behavior of posters in these different forums, where a single user will display differing rates of toxicity based on the forum they are posting in (Almerekhi, Kwak, & Jansen, 2020).

Frequently these established norms can align the community against people belonging to marginalized groups (Cullen, 2022). And where communities are especially vocal or abusive it can act as a gate-keeping mechanism to force people from marginalized groups to behave or speak only in ways consistent with what the community is willing to tolerate (Beres et al., 2021; Cullen, 2022).

Normalization of toxicity creates in people beliefs that toxicity is a subjective thing; whether a comment elicits perceptions of toxicity is independent of whether a comment is toxic. As concluded by Jurgens, Chandrasekharan, & Hempill (2019) though, we cannot improve conditions of toxic online environments as long as we have different, competing standards for defining toxic language and behavior.

Mitigation of toxicity

The standard for mitigation of toxicity in forums is effective moderation. Good moderation is capable of warning users ahead of time in a stickied thread that toxic comments will not be tolerated, directly removing toxic comments, and banning frequent offenders. The results of a study by Srinivasan et al., 2019 showed that effective moderation, although not a significant contributor to future reformed behaviors, did significantly contribute to reduced future rates of toxic comments.

This first prompt is used to set the expectations for the generative AI. It stipulates what you will be providing (which can work as error correction if you then provide something other than what you state you will), as well as lets it know to just hold onto that text until you request something. This is useful in cases where the amount of text you are including exceeds the character limit of the AI. In those cases you can either state that you are giving it a document in parts, or a better feature largely available now is to put the document into a PDF and upload that.

You
I am going to begin by giving you text from an article I wrote. Don’t do anything with it yet.

With the text fed into the working memory space you can now make specific requests. You can see this first question is very generalized and can work with any text to develop questions more suitable for formative assessment or by a student to check their comprehension.

You
from this text I need to generate a set of multiple choice questions to assess student comprehension of the material for a class of college students. please use the text and begin by generating 20 multiple choice questions along with the correct response.

Here that prompt is refined to focus narrowly on a specific subject within the text. This prompt could just as easily be phrased in a way to permit you to have a conversation with the document. “How does the normalization of toxicity contribute to toxicity towards women in online gaming.”

You
I would like 5 questions that focus on the effects of normalization of toxicity and how that contributes toward toxicity towards women in online gaming. Please generate 5 open ended questions, and then generate 5 multiple choice questions with the correct response listed.

P1:
Breakout room designed for small group discussion, increase engagement class. Usually unsupervised, people not doing there work.
Feel different when full of productive people, but not usually the case. Usually everybody was doing their own stuff, and the person picked in charge will work out everything by their self, and only talk their own stuff. Making things up.
Very productive people and exhausted. Sometimes everybody is discussing, but time zone makes S feels sleepy. Usually, can keep up with the peace, but sometimes not. Two challenges, 1) feel cozy and sleepy 2) discussion content not interesting.
Feel shamed for not participating well.

P2:
Like:
1, random: you won’t have to pick which one, sometimes you don’t know who the people are.
Dislike:
1, random people, not willing to talk, you and someone else talking. The third person won’t communicate.

Fine with breakout room and random people, certain type of people does not contribute even being called on. Maybe just sleeping in, not answering. Impact: try to talk to a person who is not talking. Not really affect much.

P3:
Like it. Nice to sperak to people. Loss that in virtual class.
Avg. 5 people. C-1. Biologo 2 people (hard, sometime don’t know how to talk, call for help). Samler better social interactions. Turn off camera and not resounding (7+ apply to this).

P4:
Prefer listening to lectures. Medium shy, don’t want to talk in class.

P5:
Like when you have some time for breakout group if other people are not engaged. People don’t speak a lot in the main class but will speak in small groups.
Dislike if other people are not engaged in the class. S is in the middle, not very engaged but still will work with students to talk. If actual discussion, will want to be part of it. Awkward and quiet, will feel uncomfortable.
Don’t like how you have to present in front of people. Pressure goes away if a lot to say (quality discussion). If no one else, S hate to fake a discussion. Nothing to do with the difficulty of the question.

P6:
Dislike.
1. don’t like having a spotlight in the class. In the chem breakout rooms, Peer mentor to coming in to check if camera on and talking. PM not utilized correctly, not policing.
2.Didn’t care to be forced to patriciate.
3 Prefer individual work when struggling
4 Bad experience other people don’t respond. At the end of the semester, nobody participate except S. Worse in smaller group.
6 Breakup the class time. Dislike STEM class approach at UNC Self-teach hard questions,
7 If not paying attention to class anyway, breakout group force you to pay attention. S stopped hearing to keep up the appearance. Don’t know how to facilitate. Encouraged to know peer better. Mightbe easier. Feel like people in the class in physics studio.

P7:
Like it.
1. Discussion close to in-person experience.
2. Make S feel engaged.
3. Learned more and know classmates.

P8:
Overall, don’t like

Like:

Smaller group, takes the pressure off induvial students from know everything. Connect to other students, don’t feel lonely.

Slowing down peace. Lecture went fast, having hard time keeping up. Nervous. Being able to ask questions to P and fellow students on a smaller level.

Dislike:

Don’t know people, awkward. In spring, don’t go back to campus. 2hrs away from chapel hill, some people are already friends with each other. Breakout go over and turned off sound and stay in silence.

Sometimes no one talks. Once or twice in Linguistic 290. One group will always have two people. If a good teammate (interactive and engaged), will speak up. Teammate (not interactive and not engaged), S doesn’t want to talk.

P9:
Like the breakout group.
Besides work, have extra conversation with class. Nice to mimic in-person small groups. S feel like knowing people.
One class annoying, assignment/topic was not clear in terms of instructions and P expectations.

Don’t like the people in the group, person not conducting inappropriately. Personality was not social affect the experience.

Again, we begin by setting expectations. This could be further improved by specifically stating “there are 9 total responses”. What this next does is define some sort of delimiter so that a batch of responses can be uploaded rather than entering each one at a time.

You
I have conducted a survey of student opinions on the use of the breakout room function of Zoom for online course. Each participant answer is coded as P1 through P9. I will first give you each response.

In this prompt we specify what we want as well as the parameters. Once you have a response you can supply additional prompts to ask how many responses liked or disliked a particular trait, or ask for a list of responses that shared some response in common so that you can do follow-up analyses of similarities between those responses.

You
I would like you to summarize what it is students most like about breakout rooms and what they most dislike about breakout rooms. Include 3 items for what is most liked and 3 items for what is most disliked.

Dear Admissions Committee:

I am writing this letter on behalf of [Student Name], to give her my highest recommendation for your program. [Student Name] was a student in my [Course Name] during the Spring 2023 semester, and throughout that time she demonstrated the diligence, motivation and analytical thinking skills necessary for success: all traits that will serve her well as she continues her education and pursues her goals of obtaining a degree in Physical Therapy.

Over the course of my career I have had the opportunity to advise and teach numerous undergraduate, graduate and post-doctoral level research assistants, laboratory personnel and students. Without a doubt, [Student Name] is one of the most intelligent and hardworking undergraduate students with whom I have ever had the pleasure to interact. My [Course Name] is an advanced elective, and the students who generally register are those that seek knowledge beyond our program’s required curriculum. This spring, we had a small class size, which included both doctoral and undergraduate students. The first half-hour of each class was dedicated to discussion of assigned readings, which are intentionally chosen to be challenging and push the students out of their comfort zone. With the small class size, each student had a greater responsibility to be fully involved in every discussion. [Student Name] came to each class prepared with questions, and participated heavily in every discussion. She consistently demonstrated an ability to tackle challenging questions presented to the class, and an ability to think on her feet as she worked through a given problem while in front of her peers. Writing is also a main component of the course, and [Student Name] already writes at a graduate level. Her writing is extremely well thought-out and she challenges herself by taking on topics that broaden her understanding of ideas and concepts. In the end, [Student Name] outperformed several graduate students in my class.

Outside of class, [Student Name] was incredibly active through her work both in healthcare and her extracurricular activities. She participated in clinical experiences in various roles here in the [University Name] Healthcare system and outside, gaining invaluable experience that will prepare [Student Name] for her long-term professional goals in physical therapy. In addition, she has maximized her time in a variety of extracurricular activities, including [Details Here]. She did all of this while also maintaining a high GPA, and graduating with Highest Distinction from our program.

It is for all of these reasons that I give [Student Name] my highest recommendation for continuing her education in a physical therapy program. The best recommendation I can give for an undergraduate student is that I would want to accept them into a doctoral position to work with my own laboratory, and [Student Name] is exactly the type of student that I look for on my own team. Her curiosity and personality will make her a joy to work with, and she has demonstrated graduate-level capabilities in her writing, presentation and high-level conceptual understanding related to my course content. I believe [Student Name] to be an exceptional candidate for your physical therapy program and I predict that she will have an extremely bright future.

The student’s name is [Student Name].

• The structure of the [Course Name] and its inclusion of both undergraduate and PhD students could highlight my eagerness to learn and face challenges.
• My capacity to express ideas clearly and demonstrate knowledge. A potential discussion topic could be my term paper/presentation where I examined the initiation of gait and the related issues in Parkinson’s disease from the perspective of coordination dynamics. This could be linked to my interest in physical therapy and clinical intervention.
• A more general personal characteristic is my authentic ability to collaborate with others, demonstrating confidence, reliability, and respect. This could include my receptiveness to constructive feedback.”

Sample resume:

EDUCATION

[UNIVERSITY NAME] City, State

Bachelor of Science, Biology, Health and Wellness, Minor in Nutrition May ####

High Distinction, Dean’s List, GPA: 3.92

Honors: Dean’s List

Activities: Member, [Fraternity Name]; Student Leader and Volunteer, [Student Center Name]; Member, Pre-Med Club; Mentor, Biology Department.

WORK EXPERIENCE [PHYSICAL THERAPY CLINIC NAME]

Physical Therapy Assistant

• Assisted Physical Therapists in guiding patients through exercises
• Managed clinic inventory and equipment and handled necessary administrative tasks

VOLUNTEER EXPERIENCE [FRATERNITY NAME]

Member

City, State July 2023 – Present

City, State January 2021 – May 2023

• Participated in charity work for Environmental Conservation, including local initiatives in City with ‘Green City Project’ and the ‘Nature Center’.

[STUDENT CENTER NAME] City, State Event Coordinator, Sunday School Teacher, Retreat Worker August 2019 – May 2023

• Organized and led weekly gatherings for college students.
• Developed lesson plans and taught 3rd – 5th graders at a Catholic school.
• Assisted in planning a weekend retreat and ensured its successful execution.

PEER MENTORING City, State Biology 101 Peer Mentor January – May 2022

• Attended the biology lecture for students to ask questions before, during, and after class.
• Assisted a graduate teaching assistant in leading a problem-solving class related to biology.

[LOCAL SPORTS CLUB] City, State Coach March – May 2022

• Coached coed 5-year-old youth basketball, teaching the basics of basketball and teamwork. PHYSICAL THERAPY OBSERVATION EXPERIENCE

[Rehab Center Name], City, State (80 hours) June – July 2022

• Observed outpatient evaluations and treatment plans, including occupational therapy.
• Helped in setting up and cleaning treatment stations.

[Local Hospital Name], City, State (25 hours) August 2022

• Observed acute physical therapy treatment in a hospital environment.
• Shadowed the process of taking notes during evaluations, reevaluations, goal setting, and treatment sessions.

ADDITIONAL INFORMATION

Certification: Adult and Pediatric First Aid/CPR/AED, American Red Cross”

In this case you are stating who you are and what your duties are. This is allows the generative AI to assume a persona – along with a appropriate schemata – to generate the response. You can try for instance to insinuate that you are a high school teacher, or that the recommendation is for a different type of program to see how responses vary.

You
Assume the role of a faculty member at a higher education institution in the [Department Name]. Your responsibilities include teaching a course titled [Course Name], which is attended by both senior undergraduate students and graduate students. One of the undergraduate students has requested that you write a general letter of recommendation for her application to a physical therapy program.

The student has provided her resume and some key points she would like you to paraphrase and incorporate into the letter.

I will begin by providing those points from the student. Hold those for now and don’t reply until I give you the next pieces.

The key thing provided here is a template – the sample recommendation letter. Templates have a lot of utility, and are able to either create text to match a given specification, or transform text from one format to another – for instance taking narrative text and aligning it to a form.

You
I will give you her resume next, followed by a sample recommendation letter written for another student that you can use as a guide. After and only after I have given both I would like you to craft the general letter of recommendation for her application to a physical therapy program.

Research example:

Note – if using Bing Chat through campus you will need to break this up into 3-4 pieces. With GPT-4 it can be done in a single copy/paste.

 

Background & Importance

In 2022, over 200 million individuals across the country reported playing video games regularly, with an average weekly gaming time of 13 hours. While “gaming” is a broad term that includes both recreational and competitive levels, esports, or organized, competitive video gaming, is a distinct subset. Esports differs from regular computer use, such as office work, due to the higher input demands, with competitive players reaching up to 400 actions-per-minute. Esports is a rapidly growing digital phenomenon worldwide, spanning multiple levels of play, including high school, college, and professional competition. A local university has also seen development in this area with the introduction of a Gaming Arena and expansion of the University Esports Club.

However, the growing popularity of esports brings new health-related issues for its participants. Increased screen time leads to more sedentary behavior, and over 40% of gamers and esports athletes report musculoskeletal pain in the neck, back, shoulders, hands, or wrists. Eye health is another major concern, with increased screen time associated with more eye discomfort. Computer Vision Syndrome (CVS), a medical condition associated with exposure to digital screens, causes symptoms such as increased tearing, headaches, and blurred vision after prolonged use. Eye fatigue is the top physical complaint of collegiate esports players, with over 50% reporting ocular symptoms. Additionally, esports athletes often have abnormal sleep patterns and a high prevalence of sleep disturbance.

Despite these health concerns, very few esports players and gamers seek care from a medical professional. This has resulted in large gaps in the medical literature regarding esports and gamer health. While musculoskeletal pain and eye health are documented medical issues in this population, few specifics are known about the predominant symptomology of these conditions or their successful treatment and prevention. Consequently, there is minimal information available to develop evidence-based guidelines for the healthcare of esports players and recreational gamers.

Project Goals and Outcome Metrics

The aims of this proposal are to evaluate the potential influence of eye fatigue/CVS on objective measures of visual-motor performance, a skill set linked with high-level esports performance, and assess any potential association between eye movement patterns during gameplay and the incidents and/or severity of CVS symptoms. Once delineated, these data will be the springboard for additional research questions exploring CVS treatment and prevention strategies, as well as methods for improving visuomotor performance, to inform esports players of all levels. We anticipate that, upon completion, the presented proposal will result in the production of multiple published manuscripts.

The proposed study will address the following research questions:

• RQ1: Is there a negative association between the incidence/severity of self-reported CVS symptoms and visual-motor performance in collegiate esports players?
• RQ2: Is there an association between the incidence/severity of self-reported CVS symptoms and certain eye movement patterns while playing esports in collegiate esports players?
• RQ3: Is there a negative association between the incidence/severity of self-reported CVS symptoms and visual-motor performance in competitive collegiate esports players after 120 minutes of esports participation?
• RQ4: Is there a positive association between planned ocular breaks during 120 minutes of esports participation and the incidence/severity of self-reported CVS symptoms in competitive collegiate esports players?
• RQ5: Is there a positive association between planned ocular breaks during 120 minutes of esports participation and visual motor performance in competitive collegiate esports players?

 

Project Description, Approach, Methods

Participants: Twenty (18-24 years) healthy collegiate esports players will be recruited. Participants will be excluded if they are not an active member of a competitive collegiate esports team or have experienced an ocular, neurologic, vestibular, or dominant upper extremity injury within the proceeding 6 weeks.

Study Design: This study will involve esports players participating in approximately 120 minutes of competitive esports play that is outside of a formal competitive event, referred to as a “scrimmage”. This time interval was chosen based on an average game duration of about 30 minutes for the majority of popular esports titles with most competitive events utilizing a “Best of Three” or “Best of Five” structure. Therefore, the average total time of computer screen exposure during a typical esports competition is about 120 minutes. We will utilize a repeated-measures cross-sectional study design at two within-session timepoints (pre- and post-scrimmage). The experimental session will take place in a local Gaming Arena and will last approximately 160 minutes (including pre/post collection times and scrimmage duration). The Computer Vision Syndrome Questionnaire (CVS-Q) will be used as a subjective measure to assess participant’s perceived CVS symptoms. Senaptec Sensory Performance tablets and their proprietary application will be used to obtain objective measures of visual-motor performance to investigate any potential impact of eye fatigue on this metric. Data collection will begin pre-scrimmage and will be obtained in the following order: (1) CVS-Q and (2) visual-motor performance modules on Senaptec tablets. Following completion of the pre-test assessment battery, players will then take part in a game-specific scrimmage lasting 120 minutes. At the completion of the scrimmage, each player will again complete the assessment battery in reverse order: (1) visual-motor performance modules on Senaptec tablets) and (2) CVS-Q. For the final phase of the study, players will return to the local Gaming Arena to individually compete in a single game of the same esports title used in the previous phases during which eye movement patterns will be obtained via a Tobii Pro Nano screen-based eye tracker.

Measurements

CVS-Q: The CVS-Q is a reliable and validated tool for assessing the prevalence of CVS by evaluating the frequency and intensity of 16 symptoms. Data collected on the CVS-Q is used to calculate a total score ranging from 0-12. A score of greater than or equal to 6 is considered consistent with a diagnosis of CVS.

Visual-Motor Performance: Utilizing Senaptec Sensory Performance tablets and their proprietary application, players will be assessed on six dimensions of visual-motor performance: eye-hand coordination, go/no-go, response inhibition, spatial memory, spatial sequence, and multiple object tracking.

Eye Movements Patterns Tracking: Tobii is a global leader in eye tracking technology that has developed screen-based eye trackers to capture accurate details of eye movement patterns, including blink rate, gaze point, and pupil date. The Tobii Pro nano screen-based eye tracker model was designed with research indications in mind and has been used in several published studies.

Statistics: A preliminary power analysis related to Ocular Discomfort Total Score (with components similar to the CVS-Q which has been validated, as previously stated) indicate that a sample size of 20 subjects will provide power = 0.94 (based on the previous study’s high effect size; cohen’s d = 0.83) at an alpha level = 0.05. Thus, the proposed sample size of N = 20 is more than adequate to test the study research questions. Data for RQ1 and RQ2 will be analyzed using individual paired-samples t-tests to compare within-subject means between the pre- and post-assessment time points. RQ3 will be analyzed by submitting all six visual-motor performance measures to a one-way repeated measures multivariate analysis of variance (MANOVA) to determine within-subject differences across the pre- and post-assessment time points.

In this case you are simply going to tell the AI what it is you are trying to create and ask it how you would do it. If this is something you’re completely unfamiliar with you might not know to include that you’d like to use the “openai chatgpt api” – and in fact I first discovered that I would need to use it based on asking this question. I was then able to inject that piece of information into later prompts.

This prompt is a great example just because it is a place where most people cannot use prior knowledge. It shows how you can start with a “simple” question and continue to ask questions about the different steps, or how to do particular things.

As a follow-up you might stipulate that it answer this request for you as a person who knows varying amounts about computer programming.

You
using the openai chatgpt api I would like to create a browser interface that loads a predetermined prompt that others can interact with. the people interacting with the interface should not be able to see what prompt I gave to the api. how do i do this?