背景技术众所周知,自闭症谱系障碍(ASD)的患病率和诊断在男性中比在女性中更常见。尽管如此,在研究应用行为分析(ABA)对各种测量变量的治疗效果的性别差异的自闭症研究中,存在显著差距.本研究旨在全面评估有关目标行为目标的性别差异,目标,和赤字变量。材料和方法本研究分析了100名参与者的回顾性数据,包括89名青少年和4名成年人,有七个案例缺乏年龄记录,从3月19日至2023年6月11日接受了为期三个月的ABA计划。ABA计划包括各种方法,如功能分析,离散试验培训,大规模审判,和自然主义训练。成果计量数据,包括目标行为能力,年龄,熟练程度的平均试验,熟练程度的平均教学天数,开放的行为目标,和目标趋势,是使用“催化剂”软件收集的(催化剂软件公司,纽约,NY).使用分类(性别和种族/种族)和连续变量(已掌握行为目标的百分比,年龄,平均试验,平均教学天数,开放目标,维护期间失败目标的百分比,向上的目标百分比,向下,和平坦的趋势)。这些统计数据包括平均值,标准偏差,中位数,和范围,并使用9个单独的两样本独立t检验和相应的效应大小进行推断分析。结果在所有检查的9个变量中,基于性别的差异无统计学意义(p>0.05):年龄,掌握的平均试验,掌握教学的平均天数,打开目标,维修失败的目标百分比,趋势上升的目标百分比,下降的目标百分比,和目标趋于平坦的百分比,并检测到广泛的置信区间。结论关于这九个行为目标,对ABA治疗的反应无显著性别差异,掌握,赤字变量可能是相关的。他们认为ABA治疗对男性和女性自闭症个体同样有益。这些结果应谨慎解释。观察到的一般模式,以广泛的置信区间为特征,带有一定程度的统计不确定性,这可能表明了巨大的性别差异。这些结果可能会质疑关于基于性别的治疗反应变化的普遍信念。这可能会对临床实践产生深远的影响,暗示医疗保健专业人员在建议ABA疗法时不应该偏爱一种性别。相反,治疗建议应针对每个孩子的独特要求和特点,不分性别。研究人员希望这些结果能鼓励该领域的其他研究。了解影响治疗反应的因素对于改善治疗结果和定制护理至关重要。
Introduction It is widely recognized that the prevalence and diagnosis of autism spectrum disorder (ASD) are more common in males than in females. Despite this, there is a significant gap in the body of autism research that investigates gender differences for treatment effects of applied behavior analysis (ABA) across a variety of measured variables. This research aims to comprehensively evaluate gender distinctions concerning target behavioral objectives, goals, and deficit variables. Materials and methods This study analyzed retrospective data from 100 participants, including 89 juveniles and four adults, with seven cases lacking age documentation, who underwent a three-month ABA program from March 19 to June 11, 2023. The ABA program included various methodologies such as functional analysis, discrete trial training, mass trials, and naturalistic training. Data on outcome measures, including target behavioral proficiency, age, average trials to proficiency, average teaching days to proficiency, open behavioral objectives, and target trends, were collected using the \"Catalyst\" software (Catalyst Software Corporation, New York, NY). Participant demographics were summarized using statistical analyses for categorical (gender and race/ethnicity) and continuous variables (percentage of mastered behavioral objectives, age, average trials, average teaching days, open objectives, percentage of failed objectives during maintenance, percentage of objectives with upward, downward, and flat trends). These statistics included mean, standard deviation, median, and range and were analyzed inferentially using nine separate two-sample independent t-tests and corresponding effect sizes using Cohen\'s d. Results There were no statistically significant disparities based on gender (p > 0.05) across all nine variables examined: Percentage of Targets Mastered, Age, Average Trials to Mastery, Average Teaching Days to Mastery, Open Targets, Percentage of Targets Failed in Maintenance, Percentage of Targets Trending Up, Percentage of Targets Trending Down, and Percentage of Targets Trending Flat, and wide confidence intervals were detected. Conclusions Non-significant gender differences in response to ABA treatments regarding these nine behavioral goals, mastery, and deficit variables may be relevant. They suggest that ABA treatments could be equally beneficial for both male and female autistic individuals. These results should be interpreted cautiously. The general pattern observed, characterized by broad confidence intervals, carries a degree of statistical uncertainty, which may suggest substantial gender differences. These results might question the prevailing beliefs about the variation in treatment response based on gender. This could profoundly impact clinical practices, implying that healthcare professionals should not favor one gender over another when suggesting ABA therapies. Instead, the treatment advice should be tailored to each child\'s unique requirements and traits, regardless of gender. The investigators expect these results to encourage additional research in this field. Comprehending the elements that affect treatment response is vital for improving treatment results and customizing care.