挑战期望:我们能识别出具有“非预期”发展结果的邻居吗?

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES International Journal of Population Data Science Pub Date : 2022-08-25 DOI:10.23889/ijpds.v7i3.1814
E. Duku, B. Forer, Molly M. Pottruff, M. Guhn, M. Janus
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引用次数: 0

摘要

为了提供幼儿园儿童发展与社区经济地位之间关系的证据,我们的目标是量化“违背预期”的社区的社会人口统计学和儿童发展特征:高经济地位社区的儿童发展结果远低于预期,而低经济地位社区的儿童发展结果远高于预期。使用探索性和基于模型的潜在剖面分析(LPA),我们使用10个SES指标确定了2038个定制加拿大社区的同质剖面组。我们确定了最简洁的轮廓组数量,并使用邻里和汇总儿童特征验证和表征了派生的邻里组。接下来,作为我们的结果,我们创建了发展脆弱性风险的四分位数组,使用早期发展工具(EDI)进行测量,以匹配派生的邻里概况组的数量。最后,我们使用列联表分析来识别违背预期的社区,然后使用描述性统计和相关分析来表征这些社区。结果LPA鉴定出4个社区社会经济地位群体,分别为“低”(31.6%)、“低-中等”(12.7%)、“高-中等”(38.4%)和“高”(17.4%)。这四个SES组与EDI漏洞风险的四分位数组交叉表。对结果的4 × 4应急表的检查显示,在“低”SES剖面组中,57个(8.9%)社区的发展脆弱性风险远远好于预期。相反,在“高”社会经济地位群体中,12个(3.4%)社区的发展脆弱性风险远低于预期。此外,这些分析还发现,各省在违背预期的社区比例方面存在很大差异。在研究的12个省份和地区中,每个省份内违背预期的社区比例从零到50%不等。结论对非预期邻里的识别有助于我们对影响儿童发育的邻里因素的理解。使用混合方法,可以将这些社区与来自相同SES概况组的邻近社区进行比较,这些社区不会违背预期,以努力确定区分它们的背景因素。
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Defying Expectations: Can We Identify Neighbourhoods with “Other Than Expected” Developmental Outcomes?
ObjectivesTo contribute to the evidence on the association between neighbourhood-level child development in Kindergarten and neighbourhood SES, our objective was to quantify the sociodemographic and child development characteristics of the neighbourhoods that “defy expectations”: high SES neighbourhoods with much-worse-than-expected child outcomes, and low SES neighbourhoods with much-better-than-expected child outcomes. ApproachUsing exploratory and model-based Latent Profile Analysis (LPA), we identified homogenous profile groups of 2038 customized Canadian neighbourhoods using ten SES indicators. We identified the most parsimonious number of profile groups and validated and characterized the derived groups of neighbourhoods using neighbourhood and aggregated child characteristics. Next, as our outcome, we created quartile groups for developmental vulnerability risk, measured with the Early Development Instrument (EDI), to match the number of derived neighbourhood profile groups. Last, we used contingency table analysis to identify neighbourhoods that defy expectations, and then characterized these neighbourhoods using descriptive statistics and correlational analysis. ResultsThe LPA identified four neighbourhood SES groups which we labelled “Low” (31.6%), “Low-moderate” (12.7%), “High-moderate” (38.4%) and “High” (17.4%). These four SES groups were cross-tabulated with quartile groups of EDI vulnerability risk. Inspection of the resulting 4-by-4 contingency table showed that within the “Low” SES profile group 57 (8.9%) neighbourhoods had much-better-than-expected developmental vulnerability risk. Conversely, within the “High” SES profile group, 12 (3.4%) neighbourhood had much-worse-than-expected developmental vulnerability risk. Additionally, these analyses identified large provincial differences in the proportion of neighbourhoods that defy expectation. In 12 provinces and territories in the study, the proportion of neighbourhoods that defied expectations within each province ranged from zero to 50%. ConclusionThe identification of neighbourhoods that defy expectations contributes to our understanding of neighbourhood factors influencing child development. Using mixed-methods approaches, these neighbourhoods can be compared to nearby neighbourhoods from the same SES profile group that do not defy expectations, in an effort to identify contextual factors that differentiate them.
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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