Over the past decade, the public, the media, state legislators, and the sports medicine community have paid a lot of attention to concussions in sports. As shown by epidemiological studies, concussions are common in sports of all levels. Increases in concussion rates (CRs), which are calculated by dividing the total number of concussions by the total number of athlete exposures (AEs) or by the number of athletes that participate in a practice or game, have sparked growing public concern (Tsushima et al., 2019). The present paper offers a thematic analysis of concussion incidents in youths from data collected in an initial report. In addition, it presents ethical considerations regarding the topic and future-directed reflection.
Planning Notes
Thematic analysis is a common qualitative research strategy for revealing hidden themes, interpretations, and connections in large datasets (Delve & Limpaecher, 2020). It works well with text-rich qualitative data like survey replies or interview transcripts.
The first step is to study the data, which consists of the survey questions and a summary of the results. The step is commonly known as the familiarization stage. Before moving on to the coding and analysis phases, it is helpful to take a step back and get a bird’s-eye view of the entire dataset and its context (Terry et al., 2017).
The second step is coding. In coding, numbers or letters identify specific categories within a larger data set (Vaismoradi & Snelgrove, 2019). Here, we may categorize replies to the survey according to the pre-set categories, such as “yes,” “maybe,” or “no” for the incidence of concussion, or by selecting “yes” or “no” for participation in various organized sports when concussed. Codes can also be made for free-form participant input, such as “Other” sources or activities.
Once coding is complete, the researcher may examine the codes and seek patterns or similarities across the dataset to discover possible themes. Essential parts of the data can be summarized by broader concepts or categories known as themes.
The fourth step entails reviewing and refining themes. This stage entails re-examining the data in light of the established themes to ensure they fairly reflect the study’s content and aims (Riger & Sigurvinsdottir, 2016). Themes can be adjusted, merged, or subdivided as needed to reflect the details of the data more accurately.
After the themes have been examined and honed, it is time to give them proper titles. This necessitates elucidating each subject and pointing forth the precise codes and data that back it up. Consistency can be maintained, and analysis findings can be better understood with unambiguous definitions and naming conventions in place.
The final step involves writing the analysis. This section concludes the theme analysis with a summary and interpretation of the results (Terry & Hayfield, 2021). As part of this, it is important to use suitable language and include relevant examples or quotes from the data to support each theme as you deliver the results.
Raw Analysis
After carefully reading and familiarizing myself with the given data set, I assigned themes and codes and finally analyzed the dataset.
The first theme is Concussion Incidence, and it is assigned codes “Yes,” “Maybe,” or “No.” In the dataset, 27.78 of the participants reported sustaining a concussion in the past year, 22.22 percent were uncertain, and 50 percent not have sustained a concussion.
The second theme is Activity during Concussion. The theme was assigned codes “School Sport,” Club or League,” “Community,” or “Other.” 46.15 percent of the participants reported having sustained a concussion while playing school sports, 30.77 percent for community teams, and 15.38 percent for a club or league. One participant reported having sustained a concussion from “Other” and did not specify the activity.
The third theme is Organized Sports during Concussion. It is assigned codes specific to a sport, such as volleyball, football, softball, and “Other.” Most participants who reported having sustained a concussion reported playing football which is 25 percent of them, 16.67 percent soccer, and 16.67 percent while cheerleading. Several participants reported that they sustained concussions from “Other” sports, including water polo, softball, baseball, and hockey.
The fourth theme is the Recognition of Concussions. It is assigned codes “I recognized it myself,” “Coach,” “Teammate,” Athletic Trainer,” “Parent,” and “Other.” Twenty-five percent of the concussions were self-recognized, 41.67 percent by coaches, and 16.67 percent by “Others.”
The fifth theme is Removal from Play. It is assigned codes “Definitely not,” “Probably not,” Might or Might not,” “Probably yes,” and “Definitely yes.” Responses from participants regarding whether they were removed from play varied. Half responded “Definitely yes,” while others were negative when asked and unsure.
The sixth theme is Entity Removing from Play. It is assigned codes “I removed myself,” “Trainer,” “Coach,” “Parent,” and “Other.” 33.33 percent of the participants removed themselves from play, while coaches removed 50 percent. The rest were removed by either a parent or a trainer.
The seventh theme is Evaluation after the Game or Practice. It is assigned codes “Physician,” “Trainer,” and “Other.” 41.47 percent of the participants were evaluated by trainers, while 50 percent by “Other.”
The ninth theme is Cleared to Play and is assigned codes “Yes,” “Maybe,” and “No.” Seventy-five percent of the participants mentioned they were cleared before resuming play; 16.67 percent were unsure, while 8.33 percent reported they were not cleared.
The tenth theme is “Duration without Participating in Athletics. It is assigned codes “1-2 days,” “3-7 days,” “8-10 days,” and “more than ten days.” 45.45 stayed between a day or two, 45.45 percent between 3 to 7 days, and 9.09 percent between 8 to 10.
The eleventh theme is “Country of origin,” all participants were from the United States (US). The twelve theme is “Age,” and the majority were between 12 and 17. Only one participant was 29 years. The fourteenth theme is Gender and is assigned codes “Male” and “Female.” 66.67 percent were Males, while 33.33 were Females. The last theme is “Individual completing the survey. It is assigned codes “Self,” “Coach,” “Parent,” and “both.” Fifty percent of the individuals were coaches, while the other 50 were parents.
Ethical Considerations
The researcher began by asking for approval from the Institutional Review Board (IRB), thus having the ethical approval to conduct the present study. They obtained informed consent from the coaches and the parents before collecting the data. They were informed of the purpose of the research, potential benefits, and risks and that they were free to participate (Suri, 2020). The researcher also assured the participants of their anonymity and confidentiality. The researchers implemented secure procedures when handling and storing the data.
Future Directed Reflection
Upon reviewing the results of the theme analysis on the prevalence and danger of concussions among young athletes, several positive factors emerged. The offered dataset provided a reasonable basis for analysis as it contained substantial information and survey responses. The research questions used to direct the study were specific enough to yield useful theme identification. In order to identify patterns and gain insights into the experiences of young athletes, thematic analysis was shown to be a useful technique.
However, weaknesses were also obvious. The lack of rich qualitative data and the absence of participant quotations limited the analysis’s breadth and illustrative power. Future research to refine the technique should prioritize gathering a wealth of qualitative data (such as participant quotations), increasing sample size and diversity, using a mixed-methods approach, polling participants for their thoughts, and guaranteeing inter-rater reliability. If these changes are made, research on concussions in young athletes will be more reliable and thorough.
References
Delve, H. L. & Limpaecher, A. (2020). How to Do Thematic Analysis. Essential Guide to Coding Qualitative Data. https://delvetool.com/blog/thematicanalysis
Riger, S. & Sigurvinsdottir, R. (2016). Thematic analysis. Handbook of methodological approaches to community-based research: Qualitative, quantitative, and mixed methods, 33-41.
Suri, H. (2020). Ethical considerations of conducting systematic reviews in educational research. Systematic reviews in educational research: Methodology, perspectives and application, pp. 41–54.
Terry, G., & Hayfield, N. (2021). Essentials of thematic analysis. American Psychological Association.
Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGEr Handbook of qualitative research in Psychology, pp. 2, 17–37.
Tsushima, W. T., Siu, A. M., Ahn, H. J., Chang, B. L., & Murata, N. M. (2019). Incidence and risk of concussions in youth athletes: comparisons of age, sex, concussion history, sport, and football position. Archives of Clinical Neuropsychology, 34(1), 60-69.
Vaismoradi, M., & Snelgrove, S. (2019). Theme in qualitative content analysis and thematic analysis.
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