Harsh Caregiving behavior amongst daycare providers (i.e., non-parental Harsh Caregiving) negatively impacts children's development across a variety of domains. As prevalences of non-parental Harsh Caregiving appear to increase worldwide, identifying its predictors is crucial for screening and intervention. The goal of this study was to identify a set of indicators and predictive rules that may accurately predict women's risk for Harsh Caregiving behavior in daycare environments. The study recruited 75 female non-parental caregivers, from the general population, who work with infants aged 0-1. Caregivers filled out self-report questionnaires including a Harsh Caregiving measure as well as a broad variety of potential predictors. To elucidate combinations of input variables that are predictive of non-parental Harsh Caregiving, we used machine learning Decision Three Inference and CHAID algorithms. Study results revealed a predictive model including 27 questions and four different prediction paths. For example, the first path indicated that women who reported low levels of attention deficit and hyperactivity problems and low levels of rigid-negative caregiving philosophies, had 100 % chance to report low levels of Harsh Caregiving behavior. Overall classification accuracy for "High Harsh Caregiving behavior" was 95.2 %. After replication in larger samples, the model can be used as a screening tool for women expressing their wish to work with infants. Women at risk can either be declined employment or alternatively receive targeted supervision throughout their work with small infants.

中文翻译：

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一种机器学习方法，用于识别非父母照顾者在日托中心对婴儿进行严厉照顾的风险

日托服务提供者的严厉看护行为（即非父母的严厉看护）会对儿童在各个领域的发展产生负面影响。随着全球范围内非父母严厉照顾的流行率似乎有所增加，确定其预测因素对于筛查和干预至关重要。本研究的目的是确定一组指标和预测规则，可以准确预测女性在日托环境中出现严酷护理行为的风险。该研究从普通人群中招募了 75 名女性非父母照顾者，她们负责照顾 0-1 岁的婴儿。护理人员填写了自我报告调查问卷，包括严酷护理措施以及各种潜在的预测因素。为了阐明可预测非父母严厉照顾的输入变量组合，我们使用了机器学习决策三推理和 CHAID 算法。研究结果揭示了一个包含 27 个问题和 4 个不同预测路径的预测模型。例如，第一条路径表明，报告注意力缺陷和多动问题水平较低以及严格消极护理理念水平较低的女性，有 100% 的机会报告严厉护理行为水平较低。 “高度严厉的护理行为”的总体分类准确率为 95.2%。在更大的样本中复制后，该模型可以用作表达希望与婴儿一起工作的女性的筛选工具。处于危险中的妇女要么被拒绝就业，要么在照顾小婴儿的整个过程中接受有针对性的监督。