Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.

BACKGROUND: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. PURPOSE: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics. RESULTS: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. CONCLUSION: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated.

Development and Evaluation of a Web-Based Family-Centered Decision Support Tool About Firearm Storage.

BACKGROUND: Many youth in the USA have access to a loaded and unlocked firearm in their home. Discussions between adult family members have the potential to positively influence firearm storage. PURPOSE: Work with firearm-owning parents to develop and obtain preliminary data about the efficacy of a family-centered decision support tool about firearm storage. METHODS: We adapted the Ottawa Personal Decision Guide for Two to the issue of firearm storage, producing the Family Safety Check-In. Subsequently, we evaluated its acceptability and impact on intentions to make a plan with their partner (or other adult sharing parental duties) about safer firearm storage. Participants were parents/guardians of at least one school-aged child, had firearms in their home, and resided in the USA (n = 402). They completed a web-based pre-test survey, were randomized to the Family Safety Check-In or an American Academy of Pediatrics (AAP) informational resource about firearm storage for families, and then completed a post-test survey. RESULTS: The Family Safety Check-In had higher ratings for acceptability (B = 1.24, 95% CI = 0.58-1.90) and appropriateness (B = 1.46, 95% CI = 0.72-2.19) than the AAP resource. Participants in the Family Safety Check-In condition also had greater intentions to make a plan with their partner about storing firearms more safely (OR = 1.90, 95% CI = 1.26-2.87). CONCLUSIONS: The Family Safety Check-In is a promising direction for harm reduction that warrants further evaluation.

The purpose of this study was to develop and obtain preliminary data about the efficacy of the Family Safety Check-In, a family-centered decision support tool about firearm storage. To develop this family-centered intervention, we adapted the Ottawa Personal Decision Guide for Two to the issue of firearm storage. Subsequently, we evaluated its acceptability and impact on intentions to make a plan with their partner (or other adult sharing parental duties) about safer firearm storage. Participants were parents/guardians of at least one school-aged child, had firearms in their home, and resided in the USA (n = 402). They completed a web-based pre-test survey, were randomized to the Family Safety Check-In or an American Academy of Pediatrics (AAP) informational resource about firearm storage for families, and then completed a post-test survey. The Family Safety Check-In had higher ratings for acceptability and appropriateness than the AAP resource. Participants in the Family Safety Check-In condition also had greater intentions to make a plan with their partner about storing firearms more safely. The Family Safety Check-In is a promising direction for harm reduction that warrants further evaluation.

Shared Decision-Making About Returning to Sport After Recovery From Pediatric Concussion: Clinician Perspectives.

OBJECTIVE: Describe how sports medicine clinicians support decision making about sport participation after concussion recovery with adolescent patients and their parents. Specific areas of inquiry related to how clinicians framed the decision, what factors they considered in how they approached the decision process, and how they navigated discordance within families. DESIGN: Qualitative study. SETTING: Tertiary care sports medicine clinics at 4 children's hospitals in the United States. PARTICIPANTS: Individual interviews were conducted with 17 clinicians practicing in sports medicine settings. INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: Semi-structured interviews explored clinician approaches to supporting decision making, with the question guide informed by components of the Ottawa Decision Support Framework. RESULTS: Clinicians routinely incorporated aspects of shared decision making (SDM) into their conversations with families. This included ensuring all parties were informed about risk and aligned behind a shared value of adolescent well-being. Mediation strategies were used to manage discordance between adolescents and their parents, and between parents. These strategies aimed to facilitate a decision that was adolescent centered. When clinicians believed that there was a medical benefit to modifying the adolescent's sport participation practices, or when they did not believe the athlete was psychologically ready to return to the sport in which they were injured, they initiated conversations about alternative activities. In such situations, they used persuasive communication practices to encourage families to strongly consider this option. CONCLUSION: The strengths and strategies used by sports medicine clinicians in this study provide a foundation for guidance or intervention development aimed at supporting SDM after concussion with adolescents and their families.

Family and developmental correlates of adolescent involvement in decision making about screen use.

Objective: Assess how child involvement in making rules about screen time relates to age, child prosocial functioning, and amount of screen use. Methods: NORC's AmeriSpeak Panel was used to recruit a nationally representative sample of parents or guardians of school-aged children (age 5-17) in the United States (n = 2084). Parents completed survey that included measures of screen time, child involvement in rule making about screen use, family functioning, and dimensions of child psychosocial functioning. Results: Across all age categories, most families had some form of rules about the allowable amount of screen-based digital media for uses other than schoolwork: 86% of elementary school-aged children (ages 5 to 10), 81% of middle school-aged children (ages 11-13), and 61% of high school aged children (ages 14-17). Across all age groups, having rules was associated with fewer hours of screen time (elementary school: B = -1.31, 95% CI = -1.80 to -0.81, p < 0.001; middle school: B = -1.40, 95% CI = -2.20 to -0.59, p < 0.001; high school: B = -0.97, 95% CI = -1.68 to -0.27, p = 0.007). Child involvement in making rules was significantly greater for high school students as compared to elementary school students (ß=0.12, p < 0.001), and not associated with high school- or middle-school aged child screen time. Across all age groups, child involvement in making rules was associated with higher levels of prosocial functioning (elementary school: ß=0.07, p < 0.001; middle school: ß=0.19, p = 0.001; high school: ß=0.21, p < 0.001). Conclusions: Child involvement in making rules about screen use may be an opportunity to strengthen developmentally important competencies, as part of a broader autonomy-supportive approach to parenting.