Abstract
Caregivers frequently turn to social media for pediatric seizure guidance, yet it remains unclear whether high-reach content aligns with established first-aid recommendations. We conducted a cross-sectional analysis of 150 high-view seizure-related videos across TikTok, Instagram, and YouTube (50 per platform). Videos were coded for creator type, accuracy, emergency department (ED)/911 guidance, and Global Quality Score (0-5). Overall accuracy was high (88.7%); however, only 49.3% included ED/911 guidance. Educational quality differed by creator type (F = 6.15, P < .001), with Health Care Professional videos demonstrating higher Global Quality Scores than Parent/Caregiver and Influencer content. Parent/Caregiver videos achieved the highest mean views (2.88 million), although median engagement was substantially lower than that of Health Care Professional and Health Organization videos, indicating that the higher mean reach was driven by a small number of viral caregiver posts rather than consistently higher engagement. Accuracy and quality were not independently associated with views. These findings suggest that engagement dynamics, rather than educational completeness, drive reach, underscoring the importance of clinician presence in digital seizure education.
Seizures and epilepsy are among the most common neurologic disorders in childhood, with reported incidence rates ranging from 41 to 187 per 100 000 children and the highest incidence occurring during the first year of life before declining through childhood. 1 Because many seizures occur outside medical settings, caregivers are often responsible for providing immediate first aid and determining whether emergency medical evaluation is necessary.
Guidance from the American Academy of Pediatrics (AAP) emphasizes key principles of pediatric seizure first aid aimed at reducing harm and supporting appropriate escalation of care. 2 These recommendations include maintaining a safe environment during the seizure, positioning the child to protect the airway, avoiding potentially harmful interventions such as restraining the child or placing objects in the mouth, monitoring seizure duration, and recognizing indications for emergency medical evaluation, particularly for prolonged seizures, recurrent events without recovery, or associated respiratory compromise.
Prior studies demonstrate that parental anxiety and uncertainty are common following a child's first seizure, and caregivers frequently report limited understanding of appropriate management and emergency response. 3 This uncertainty is reflected in health care utilization patterns, as many children experiencing a first seizure are brought to the emergency department despite most events being self-limited. As a result, caregivers often seek additional sources of information to understand seizure management and guide future decision making. In parallel with broader shifts in digital health communication, social media platforms have emerged as prominent sources of pediatric health information, with many parents using these platforms when making health decisions for their children. 4
Social media platforms such as TikTok, Instagram, and YouTube disseminate short- and long-form videos that can reach millions of views rapidly. Although video-based content may make medical information more accessible, the absence of standardized review processes raises concerns about accuracy, completeness, and potential harm.5,6 Health-related content quality on social media varies widely, and engagement metrics (eg, views, likes) do not consistently correlate with adherence to evidence-based recommendations.
Despite growing attention to health information on social media, limited research has specifically examined the accuracy and educational quality of pediatric seizure first aid content, including whether highly engaged videos align with established first-aid guidance and recommendations regarding emergency department utilization and 911 activation. As seizures are time-sensitive and potentially high risk, incomplete or misleading first-aid guidance may affect caregiver decision making and emergency service activation. This study aimed to evaluate the accuracy, educational completeness, inclusion of emergency department (ED)/911 guidance, and engagement characteristics of pediatric seizure–related videos across major social platforms. By examining content quality and reach, this analysis seeks to clarify whether widely disseminated seizure first-aid information aligns with established clinical recommendations and to identify opportunities for improved clinician engagement in digital seizure education.
Materials and Methods
Study Design and Video Identification
We conducted a cross-sectional content analysis of publicly available social media videos addressing pediatric seizures and seizure first aid. The study evaluated informational accuracy, educational completeness, inclusion of ED/911 guidance, vaccine-related content, and engagement characteristics of high-view pediatric seizure–related videos across TikTok, Instagram, and YouTube.
Video identification and engagement data were collected on a single calendar day (January 26, 2026) to minimize variation in search rankings and engagement counts attributable to algorithmic updates. Videos were subsequently coded between January 27 and February 2, 2026, by the study investigators using the predefined coding rubric. Searches were performed using a newly created incognito account with no prior viewing history, followers, or engagement activity to reduce personalization bias.
Standardized search terms applied uniformly across platforms included “pediatric seizure,” “child seizure,” “seizure first aid,” and “what to do during a seizure.” Search results were reviewed in the order presented by each platform's native algorithm. Videos were screened sequentially until 50 eligible videos were identified for each platform according to the predefined inclusion and exclusion criteria. Because TikTok, Instagram, and YouTube do not provide the total number of searchable videos for a given query and search results are dynamically generated by proprietary algorithms, the total number of available videos could not be determined. For each platform, the 50 highest-view videos meeting eligibility criteria were included, yielding a total analytic sample of 150 videos (50 per platform). A fixed sample size was selected a priori to allow balanced cross-platform comparisons while maintaining feasibility for detailed manual review using a structured coding instrument. Similar sampling strategies have been used in prior evaluations of health-related social media content, including analyses of 45 and 102 YouTube videos.7,8 Because this study was designed as an exploratory cross-sectional content analysis rather than a prevalence study, a formal a priori power calculation was not performed.
For each video, the following variables were recorded at the time of collection: URL, upload year, total views, total likes, and video duration (seconds). Engagement metrics were documented exactly as displayed on the platform interface. All videos were viewed in full during coding.
Because this study analyzed publicly accessible content without interaction with human participants, it was reviewed by the Florida International University Institutional Review Board and determined to be exempt from formal review (IRB protocol NHSR no. IRB-26-0123).
Eligibility Criteria
Videos were eligible for inclusion if they addressed pediatric seizures (including febrile seizures, absence seizures, or infantile spasms) or seizure first aid in children; were presented in English; contained educational or informational content (verbal, written, or caption-based); were publicly accessible without login requirement; were 20 minutes or less in duration; and ranked among the highest-view results at the time of the search.
Videos were excluded if they focused exclusively on adult seizures without pediatric relevance; were non-educational (eg, fictional portrayals or purely entertainment content); were duplicate uploads across platforms; were advertisements without substantive educational content; or were non-English.
Coding Framework and Outcome Measures
A structured coding rubric was developed a priori based on established pediatric seizure first-aid guidance from the American Academy of Pediatrics (AAP), 2 the American Academy of Family Physicians (AAFP) febrile seizure review, 9 and Epilepsy Foundation first-aid standards. 10 Operational definitions were finalized before data extraction. Prior to formal coding, the 3 reviewers (A.C., R.R., and R.P.) met with the study investigators (B.D.F.S., M.I., and K.S.) to review the coding instrument, discuss operational definitions, and standardize application of the predefined coding criteria using representative examples. Each reviewer was subsequently assigned one social media platform (Instagram, TikTok, or YouTube) and independently coded all eligible videos identified for that platform using the standardized instrument. Questions arising during data extraction, most commonly regarding study eligibility or interpretation of predefined operational definitions, were discussed with 1 or more senior study investigators (B.D.F.S., M.I., or K.S.) before a final coding decision was made. These discussions served to reinforce standardized interpretation and consistent application of the predefined coding criteria across all 3 reviewers. The complete coding instrument and operational definitions are provided in Supplementary Appendix 1.
Creator Type
Creator type was treated as a categorical variable and classified based on self-identification within the video or associated profile biography as Health Care Professional (MD, DO, NP, PA, RN, EMS, paramedic), Health Organization/Hospital, Parent/Caregiver, Influencer/Non-medical, or Other/Unknown.
Accuracy Classification
Accuracy was treated as a categorical variable with 3 mutually exclusive levels: Accurate, Partially Misleading, or Incorrect/Harmful. Classification was determined by comparing video content against published pediatric seizure first-aid recommendations.2,9,10
Videos were classified as Accurate if they were consistent with guideline-based seizure first aid and contained no unsafe or misleading claims. Expected core elements included recovery positioning (side placement), avoidance of inserting objects into the mouth, avoidance of restraint, protection from injury, seizure timing, and appropriate emergency activation criteria (eg, seizure lasting ≥5 minutes, respiratory compromise, first seizure, or recurrent seizures without return to baseline). Minor omissions were permitted provided no incorrect statements were present.
Videos were classified as Partially Misleading if they contained generally correct information but included rubric-defined deficiencies such as omission of key safety steps, vague or incomplete ED criteria, overstating seizure danger, discouraging appropriate emergency evaluation, claims that antipyretics prevent febrile seizures (contrary to AAFP guidance 9 ), overgeneralization of seizure causes, confusion of seizure types, or reliance on anecdotal experience as universal advice.
Videos were classified as Incorrect/Harmful if they recommended practices inconsistent with established standards, including inserting objects into the mouth, restraining the child during seizure activity, administering food or liquids during active seizure, shaking the child, promoting home remedies in lieu of medical care, discouraging indicated emergency care, or promoting vaccine-related misinformation.
No videos met criteria for Incorrect/Harmful classification. For regression analyses, accuracy was dichotomized as Accurate vs Partially Misleading.
In addition to overall classification, individual misleading and harmful elements were recorded as separate binary variables to permit descriptive analysis of specific misinformation patterns.
ED/911 Guidance
Inclusion of ED/911 guidance was treated as a binary variable (Yes/No). Videos were coded as “Yes” if they explicitly stated when caregivers should activate emergency services using time-based or clinical criteria consistent with published recommendations.2,9,10 General statements such as “consult your doctor” without emergency thresholds were coded as “No.”
Vaccine-Related Content
Vaccine mention was coded as a binary variable (Yes/No). When present, content was further evaluated as accurate or misleading based on AAP and AAFP guidance.2,9
Neurologist Mention
Recommendation for pediatric neurology evaluation was coded as a binary descriptive variable and was not included in accuracy determination.
Educational Quality
Educational completeness was treated as a continuous variable using a modified Global Quality Score (GQS) ranging from 0 to 5. The modified GQS was informed by previously published applications of the GQS in evaluations of online health information.7,8 Unlike the original GQS, which provides a global subjective assessment of educational quality, our modified instrument operationalized educational completeness by assigning one point for inclusion of each of five predefined evidence-based seizure first-aid elements: appropriate positioning, avoidance of harmful interventions, seizure duration monitoring, explicit ED/911 guidance, and overall clarity and organization of the educational content. This modification was designed to improve transparency, reproducibility, and alignment with current pediatric seizure first-aid recommendations. Higher scores reflected greater educational completeness.
Engagement Metrics
Total views, total likes, video duration (seconds), and upload year were treated as continuous variables.
Statistical Analysis
Data cleaning and statistical analyses were conducted using Python (version 3.12.12) with the pandas, numpy, seaborn, matplotlib, scipy, and statsmodels libraries. Continuous variables, including video length, views, likes, and total score, were cleaned and converted to numeric formats. Video length was converted to seconds, and views were log-transformed to reduce skewness. Categorical variables, including platform, creator type, and accuracy, were standardized, and binary indicators were created where appropriate for regression analyses (accuracy: Accurate vs Partially Misleading; ED/911 guidance: Yes vs No). Video lengths were also categorized as <1-minute , 1-3 minutes , or >3 minutes to facilitate comparisons of total score and accuracy across categories.
Descriptive statistics were calculated for all variables. Continuous variables were summarized as means, SDs, and ranges, whereas categorical variables were summarized with counts and percentages. Spearman rank-order correlations were used to examine associations between continuous variables that were not normally distributed, including video length, total score, and video views.
To compare total scores across groups, analysis of variance (ANOVA) was performed with platform and creator type as factors. Post hoc pairwise comparisons for differences by creator type were conducted using Tukey honestly significant difference test.
Associations between categorical variables, such as accuracy or ED/911 guidance, and platform or creator type were evaluated using cross-tabulations and χ2 tests of independence. Percentages were reported both overall and within strata of platform or creator type to describe the distribution of outcomes.
Multivariable analyses were conducted to identify predictors of video quality and reach. Logistic regression was used to evaluate predictors of video accuracy, including platform, creator type, video length, and upload year. Results were expressed as odds ratios with 95% CIs. Linear regression models were used to examine predictors of total score, including platform, creator type, video length, and presence of ED/911 guidance. An additional linear regression model evaluated predictors of log-transformed video views, including platform, creator type, accuracy, and video length.
Statistical significance was defined as a 2-sided P value <.05 for all analyses. Effect sizes for 1-way ANOVA were quantified using eta squared (η2). Post hoc pairwise comparisons were adjusted using Tukey honestly significant difference test, which controls the family-wise error rate. No additional correction for multiple testing was applied because the primary analyses were prespecified.
Results
A total of 150 seizure-related videos were analyzed, including 50 from each platform (TikTok, Instagram, and YouTube). Creator types included Health Care Professionals (n = 85), Health Organizations/Hospitals (n = 39), Parent/Caregivers (n = 7), Influencer/Non-medical creators (n = 8), and Other/Unknown (n = 11). Video duration ranged from 0 to 1102 s (median 83.5 s). Global Quality Scores (GQS) ranged from 0 to 5, with a mean of 3.1 ± 0.9.
Overall, 133 of 150 videos (88.7%) were classified as accurate, while 17 (11.3%) were partially misleading. Accuracy varied by platform, with Instagram demonstrating the highest proportion of accurate videos (96%), followed by YouTube (90%) and TikTok (80%) (Table 1). Accuracy also differed by creator type, with Health Care Professionals demonstrating the highest accuracy (91.8%) and Influencer/Non-medical creators the lowest (75%) (Table 2). In multivariable logistic regression, videos demonstrated significantly lower odds of being accurate compared with Instagram (OR 0.19, 95% CI 0.04-0.93, P = .04), whereas no other variables were independently associated with accuracy (Table 3).
Video Characteristics and Accuracy by Platform. a
Characteristics of 150 seizure-related videos across 3 platforms (Instagram, TikTok, YouTube). Continuous variables are presented as mean ± SD, including video duration (seconds), Global Quality Score (GQS), views, and likes. Categorical variables are presented as counts and percentages, including accuracy classification (Accurate vs Partially misleading) and the presence of emergency department (ED)/911 guidance.
Video Characteristics by Creator Type. a
Characteristics of seizure-related videos are shown by creator type, including factual accuracy, Global Quality Score (GQS), inclusion of ED/911 guidance, and viewer engagement (views).
Multivariable Logistic Regression: Predictors of Video Accuracy. a
Results of multivariable logistic regression examining independent predictors of video accuracy. The table reports odds ratios (ORs), 95% CIs, and P values for platform, creator type, video duration, and upload year. Note: Health Organization/Hospital videos serve as the reference group for all creator type comparisons. Positive coefficients indicate higher odds of video accuracy compared to the reference group.
Despite high overall factual accuracy, educational completeness varied significantly across creator types. Educational completeness was assessed using a modified 0-5 Global Quality Score (GQS), reflecting the presence of essential first-aid components such as safe positioning, timing the seizure, avoidance of harmful interventions, and guidance on when to seek emergency care. The mean GQS across all videos was 3.1 ± 0.9. Educational quality differed significantly by creator type (ANOVA F = 6.15, P < .001, η2=0.145), representing a large effect size, but not by platform (F = 0.64, P = .53, η2=0.009), indicating a negligible effect. Health Care Professional videos demonstrated higher mean GQS (3.32) compared with Parent/Caregiver videos (2.29). Post hoc Tukey analysis confirmed significantly higher scores among Health Care Professionals compared with Influencer/Non-medical, Other/Unknown, and Parent/Caregiver creators, whereas differences involving Health Organizations were not statistically significant (Table 4). In multivariable linear regression (R2=0.227), Health Care Professional creators remained independently associated with higher GQS (β=0.42, P = .03), whereas Influencer/Non-medical creators were independently associated with lower GQS (β=–0.69, P = .03) (Table 5). Platform, video duration, ED/911 guidance, and upload year were not independent predictors of educational completeness.
Tukey HSD Post hoc Comparisons of Global Quality Score by Creator Type. a
This table presents pairwise comparisons of Global Quality Scores (GQS) between creator types using Tukey honestly significant difference (HSD) test following a significant 1-way ANOVA. For each pair of creator types, the table reports the mean difference in GQS, adjusted P value (P-adj), 95% CI (lower and upper bounds), and whether the difference is statistically significant. Positive mean differences indicate higher GQS in the first group (group1) compared with the second group (group2). This analysis identifies which creator types produce significantly higher or lower educational quality content.
Linear Regression: Predictors of Educational Quality (GQS). a
Results of multivariable linear regression assessing independent predictors of educational quality (GQS). The table reports regression coefficients (β), standard errors, t-statistics, and P values for platform, creator type, video duration, and presence of ED/911 guidance.
Video duration demonstrated a positive correlation with educational quality (Spearman ρ=0.225, P = .006) (Table 6). When categorized by length, videos lasting 1-3 minutes (mean GQS 3.19) and >3 minutes (mean GQS 3.30) demonstrated higher mean scores than videos <1-minute (mean GQS 2.89). Accuracy was highest among 1-3-minute videos (91.3%) compared with <1-minute (87.7%) and >3-minute videos (82.6%).
Spearman Correlations Between Video Characteristics and Educational Quality. a
Spearman rank correlations assessing the association between video characteristics (video duration, number of views) and educational quality (GQS). The table reports correlation coefficients (ρ) and P values.
ED/911 guidance was present in 74 of 150 videos (49.3%). The inclusion of ED guidance did not differ significantly by platform (Instagram 52%, TikTok 50%, YouTube 46%; χ2=0.37, P = .83) or by creator type (χ2=1.45, P = .83). ED guidance appeared in 51.8% of Health Care Professional videos and 42.9% of Parent/Caregiver videos.
Engagement patterns varied substantially across platforms and creator types. Parent/Caregiver videos achieved the highest mean views (2.88 million); however, median views were lower among Parent/Caregivers (9192) compared with Health Care Professionals (33 600) and Health Organizations (89 600), indicating that higher mean reach among caregivers was driven by a small number of high-view outliers rather than consistently higher median engagement (Table 2). Among the 10 most-viewed videos, 5 were created by Health Organizations, 3 by Health Care Professionals, 1 by a Parent/Caregiver, and 1 by an Influencer/Non-medical creator. Thus, 8 of the 10 highest-reaching videos were produced by medical professionals or health organizations. In regression analyses, platform was independently associated with views, whereas accuracy, GQS, creator type, and video duration were not.
Discussion
In this cross-sectional analysis of high-view pediatric seizure videos across 3 major social media platforms, overall informational accuracy was high; however, educational completeness and emergency guidance varied by creator type. Health Care Professional–generated videos demonstrated significantly higher educational quality scores than Parent/Caregiver and Influencer content, and professional expertise emerged as the strongest independent predictor of educational completeness. Notably, fewer than half of videos included ED/911 guidance, despite its central importance in seizure first-aid counseling. Engagement patterns revealed that caregiver-generated content achieved the highest mean reach but was driven by a small number of viral outliers, whereas the majority of the most-viewed videos were produced by medical professionals or health organizations. Importantly, neither accuracy nor educational quality independently predicted engagement.
These findings align with prior work demonstrating that engagement metrics do not reliably reflect informational quality. In their analysis of rheumatoid arthritis videos, Singh et al 7 reported that only 54.9% of YouTube videos were useful whereas 30.4% were misleading, and they found no significant difference in popularity between useful and misleading videos. Similarly, Çoşkun and Demir, 8 in their 2024 evaluation of circumcision-related YouTube content, found moderate quality overall and no significant correlation between engagement indicators (views, likes, video power index) and quality metrics such as GQS and mDISCERN. Together, these studies reinforce that high visibility does not guarantee reliability. Our findings extend this observation into pediatric neurology and, importantly, demonstrate this pattern across multiple platforms rather than a single site.
Prior epilepsy-focused digital analyses further contextualize our results. Brna et al 11 demonstrated that even neurologists may disagree when interpreting seizure-related YouTube videos, highlighting variability in how seizure content is perceived and classified. This underscores the complexity of evaluating seizure-related material and suggests that caregivers may face similar interpretive challenges. Additionally, Brigo et al 12 reported that online epilepsy information often varies in readability and accessibility, with implications for patient comprehension. Although readability and quality are distinct constructs, both influence how effectively digital information translates into meaningful understanding.
From a clinical perspective, the incomplete inclusion of ED/911 guidance is particularly relevant. Seizure first-aid education represents a core component of anticipatory guidance in pediatric neurology, emphasizing safe immediate management while providing clear instruction regarding when urgent medical evaluation or emergency services are warranted, including differentiation between brief febrile seizures and first-time or prolonged nonfebrile seizures requiring urgent evaluation. The observation that only half of high-view videos included explicit emergency guidance suggests a potential translational gap between established clinical counseling and digital communication. Although this study cannot determine behavioral impact, families increasingly seek information online during acute events, and incomplete guidance may influence real-time decision making.
Engagement dynamics in our study highlight the structural influence of platform algorithms. Platform was independently associated with views, whereas educational quality was not. TikTok videos demonstrated significantly higher reach independent of content rigor. This finding is consistent with prior digital health analyses demonstrating that algorithmic visibility is not necessarily aligned with informational reliability.7,8 The skewed distribution of views among Parent/Caregiver videos further illustrates viral amplification effects, in which a small number of posts account for disproportionately high engagement. However, the dominance of Health Care Professionals and Health Organizations among the top 10 most-viewed videos suggests that evidence-based content can achieve substantial reach when effectively amplified.
Taken together, these findings underscore the importance of clinician engagement in digital health spaces. Both Singh et al 7 and Çoşkun and Demir 8 concluded that physicians and professional organizations should more actively participate in online platforms to improve the quality of available information. Our results reinforce this recommendation within pediatric neurology. Health Care Professional videos demonstrated higher educational completeness without evidence of diminished reach. This suggests that evidence-based content is capable of achieving substantial reach.
This study has several strengths. It included a balanced cross-platform sampling strategy, standardized using a predefined coding instrument and a modified Global Quality Score informed by previous online health-information studies,7,8 and multivariable modeling examining independent predictors of educational quality and engagement. By focusing specifically on pediatric seizure first aid, a time-sensitive and safety-critical topic, it advances digital health research beyond general misinformation prevalence to clinically actionable insight. Nonetheless, limitations should be acknowledged. The cross-sectional design captures content at a single time point and does not account for dynamic algorithmic shifts. Engagement metrics are fluid and may change over time. 8 Quality assessment tools, including GQS, involve subjective elements, and our analysis did not measure viewer comprehension or behavioral outcomes. Additionally, the study focused on English-language, high-view videos and may not reflect lower-engagement or non-English content.
Future work should evaluate whether exposure to social media–based seizure education influences caregiver knowledge retention, anxiety, or emergency utilization patterns. Experimental comparisons between neurologist-produced and nonprofessional content could clarify how presentation style affects comprehension and action. As digital platforms increasingly function as first-line information sources, integrating evidence-based seizure first-aid messaging into platform-optimized formats may represent an important extension of pediatric neurology practice.
Conclusion
High-view pediatric seizure content on social media is frequently accurate but inconsistently comprehensive. Educational completeness and inclusion of emergency guidance vary by creator expertise, while engagement is driven primarily by platform dynamics rather than content quality. Although caregiver-generated videos can achieve substantial viral reach, professional and health organization–produced content is well represented among the highest-reaching posts and demonstrates higher educational completeness.
As social media increasingly functions as a real-time source of information during pediatric emergencies, ensuring that widely viewed content reflects clear, guideline-aligned seizure first aid is essential. Strategic neurologist engagement in digital platforms may enhance dissemination of accurate and comprehensive seizure education at scale, with potential implications for caregiver decision making and emergency care utilization.
Supplemental Material
sj-docx-1-jcn-10.1177_08830738261472020 - Supplemental material for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms
Supplemental material, sj-docx-1-jcn-10.1177_08830738261472020 for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms by Beatriz De Faria Sousa, Manal Imran, Richard Reyes, Alexis Carmona, Randy Pando and Katherine Semidey in Journal of Child Neurology
Supplemental Material
sj-pdf-2-jcn-10.1177_08830738261472020 - Supplemental material for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms
Supplemental material, sj-pdf-2-jcn-10.1177_08830738261472020 for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms by Beatriz De Faria Sousa, Manal Imran, Richard Reyes, Alexis Carmona, Randy Pando and Katherine Semidey in Journal of Child Neurology
Supplemental Material
sj-docx-3-jcn-10.1177_08830738261472020 - Supplemental material for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms
Supplemental material, sj-docx-3-jcn-10.1177_08830738261472020 for Pediatric Seizure First-Aid Content on Social Media: Accuracy, Educational Quality, and Engagement Across Platforms by Beatriz De Faria Sousa, Manal Imran, Richard Reyes, Alexis Carmona, Randy Pando and Katherine Semidey in Journal of Child Neurology
Footnotes
Ethical Considerations
The study used publicly available data and was deemed exempt by the Florida International University Institutional Review Board (approval no. 117054).
Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Author Contributions
Conceptualization: BDFS, MI, KS; Methodology: BDFS, MI, KS; Data Curation: BDFS, MI, RR, AC, RP; Formal Analysis: MI, BDFS; Investigation: BDFS, MI, RR, AC, RP; Project Administration: BDFS, MI; Writing – Original Draft: BDFS, MI, RR, AC, RP; Writing – Review & Editing: BDFS, MI, KS; Supervision: KS.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability
The data analyzed in this study were derived from publicly available social media videos. Because of platform terms of service and dynamic content changes (eg, video removal or modification), the compiled data set is not publicly archived. Deidentified analytic data are available from the corresponding author on reasonable request.
Supplemental Material
Supplemental material for this article is available online.
References
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